{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cyber Threat Hunting - Chapter 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relying on standard deviation only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.1 Jupyter notebook code — Searching for beaconing activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320044\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_original = pd.read_json(\"ch7_stream_events.json\")\n",
    "print(len(df_original))\n",
    "df = df_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320044, 85)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320044, 85)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bytes_in', '#type', 'dest_mac', '#repo', 'src_ip', '@sourcetype',\n",
       "       'endtime', '@timezone', '@rawstring', '@id', '@timestamp',\n",
       "       '@ingesttimestamp', 'timestamp', '@error', 'src_port', 'time_taken',\n",
       "       'cribl_pipe', '@error_msg', 'dest_ip', 'transport', 'bytes',\n",
       "       'transaction_id', '#error', 'dest_port', 'src_mac', 'protocol_stack',\n",
       "       'bytes_out', 'flow_id', '@timestamp.nanos', 'packets_in', 'app',\n",
       "       'packets_out', 'protocol', 'tos', 'fragment_count', 'version',\n",
       "       'protoid', 'connection', 'refused', 'tcp_status', 'client_rtt_sum',\n",
       "       'data_packets_in', 'server_rtt', 'server_rtt_packets',\n",
       "       'duplicate_packets_in', 'data_packets_out', 'duplicate_packets_out',\n",
       "       'ack_packets_out', 'ack_packets_in', 'missing_packets_in',\n",
       "       'client_rtt_packets', 'missing_packets_out', 'client_rtt',\n",
       "       'server_rtt_sum', 'initial_rtt', 'ssl_cert_self_signed',\n",
       "       'ssl_cert_sha1', 'ssl_cert_md5', 'ssl_validity_start', 'ssl_issuer',\n",
       "       'ssl_cert_sha256', 'ssl_publickey_algorithm', 'ssl_validity_end',\n",
       "       'ssl_client_hello_version', 'ssl_signature_algorithm',\n",
       "       'ssl_publickey_bit_len', 'ssl_version', 'ssl_compression_method',\n",
       "       'ssl_cipher_id', 'ssl_cipher_name', 'ssl_serialnumber',\n",
       "       'ssl_session_id', 'ssl_subject', 'canceled', 'site',\n",
       "       'http_content_length', 'server', 'uri_path', 'http_method', 'status',\n",
       "       'form_data', 'http_comment', 'http_content_type', 'uri_query',\n",
       "       'http_user_agent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bytes_in</th>\n",
       "      <th>#type</th>\n",
       "      <th>dest_mac</th>\n",
       "      <th>#repo</th>\n",
       "      <th>src_ip</th>\n",
       "      <th>@sourcetype</th>\n",
       "      <th>endtime</th>\n",
       "      <th>@timezone</th>\n",
       "      <th>@rawstring</th>\n",
       "      <th>@id</th>\n",
       "      <th>...</th>\n",
       "      <th>http_content_length</th>\n",
       "      <th>server</th>\n",
       "      <th>uri_path</th>\n",
       "      <th>http_method</th>\n",
       "      <th>status</th>\n",
       "      <th>form_data</th>\n",
       "      <th>http_comment</th>\n",
       "      <th>http_content_type</th>\n",
       "      <th>uri_query</th>\n",
       "      <th>http_user_agent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>175</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>FF:FF:FF:FF:FF:FF</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>2022-09-19T20:59:58.337959Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:59:58.337959Z\",\"time...</td>\n",
       "      <td>02WG5MlfN9D7lTOJdOA4zjpz_54_361_1663621198</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>175</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>FF:FF:FF:FF:FF:FF</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>2022-09-19T20:59:58.338787Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:59:58.338787Z\",\"time...</td>\n",
       "      <td>02WG5MlfN9D7lTOJdOA4zjpz_54_360_1663621198</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>172</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>12:34:56:78:9A:BC</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>2022-09-19T20:59:44.983553Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:59:44.983553Z\",\"time...</td>\n",
       "      <td>02WG5MlfN9D7lTOJdOA4zjpz_54_359_1663621184</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>88</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>12:34:56:78:9A:BC</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.16</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>2022-09-19T20:59:36.763847Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:59:36.763847Z\",\"time...</td>\n",
       "      <td>02WG5MlfN9D7lTOJdOA4zjpz_54_358_1663621176</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>97</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>12:34:56:78:9A:BC</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>stream:udp</td>\n",
       "      <td>2022-09-19T20:59:29.720741Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:59:29.720741Z\",\"time...</td>\n",
       "      <td>02WG5MlfN9D7lTOJdOA4zjpz_54_357_1663621169</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320039</th>\n",
       "      <td>66</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>00:22:48:2D:87:35</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>2022-09-19T20:33:23.588722Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:33:23.588722Z\",\"time...</td>\n",
       "      <td>nBCqAyE99Cb5cutkm4tiN8QR_1107_4_1663619603</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320040</th>\n",
       "      <td>228</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>12:34:56:78:9A:BC</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.15</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>2022-09-19T20:33:23.439133Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:33:23.439133Z\",\"time...</td>\n",
       "      <td>nBCqAyE99Cb5cutkm4tiN8QR_1107_3_1663619603</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320041</th>\n",
       "      <td>438</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>12:34:56:78:9A:BC</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>2022-09-19T20:33:22.803383Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:33:22.803383Z\",\"time...</td>\n",
       "      <td>nBCqAyE99Cb5cutkm4tiN8QR_1107_2_1663619602</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320042</th>\n",
       "      <td>228</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>12:34:56:78:9A:BC</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>2022-09-19T20:33:22.386591Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:33:22.386591Z\",\"time...</td>\n",
       "      <td>nBCqAyE99Cb5cutkm4tiN8QR_1107_1_1663619602</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320043</th>\n",
       "      <td>66</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>00:0D:3A:9E:2F:12</td>\n",
       "      <td>Threat_Hunting</td>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>stream:tcp</td>\n",
       "      <td>2022-09-19T20:33:20.083213Z</td>\n",
       "      <td>Z</td>\n",
       "      <td>{\"endtime\":\"2022-09-19T20:33:20.083213Z\",\"time...</td>\n",
       "      <td>nBCqAyE99Cb5cutkm4tiN8QR_1107_0_1663619600</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167294 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        bytes_in       #type           dest_mac           #repo  \\\n",
       "4            175  stream:udp  FF:FF:FF:FF:FF:FF  Threat_Hunting   \n",
       "5            175  stream:udp  FF:FF:FF:FF:FF:FF  Threat_Hunting   \n",
       "6            172  stream:udp  12:34:56:78:9A:BC  Threat_Hunting   \n",
       "7             88  stream:udp  12:34:56:78:9A:BC  Threat_Hunting   \n",
       "8             97  stream:udp  12:34:56:78:9A:BC  Threat_Hunting   \n",
       "...          ...         ...                ...             ...   \n",
       "320039        66  stream:tcp  00:22:48:2D:87:35  Threat_Hunting   \n",
       "320040       228  stream:tcp  12:34:56:78:9A:BC  Threat_Hunting   \n",
       "320041       438  stream:tcp  12:34:56:78:9A:BC  Threat_Hunting   \n",
       "320042       228  stream:tcp  12:34:56:78:9A:BC  Threat_Hunting   \n",
       "320043        66  stream:tcp  00:0D:3A:9E:2F:12  Threat_Hunting   \n",
       "\n",
       "               src_ip @sourcetype                      endtime @timezone  \\\n",
       "4           10.0.0.18  stream:udp  2022-09-19T20:59:58.337959Z         Z   \n",
       "5           10.0.0.18  stream:udp  2022-09-19T20:59:58.338787Z         Z   \n",
       "6           10.0.0.18  stream:udp  2022-09-19T20:59:44.983553Z         Z   \n",
       "7           10.0.0.16  stream:udp  2022-09-19T20:59:36.763847Z         Z   \n",
       "8           10.0.0.13  stream:udp  2022-09-19T20:59:29.720741Z         Z   \n",
       "...               ...         ...                          ...       ...   \n",
       "320039  168.63.129.16  stream:tcp  2022-09-19T20:33:23.588722Z         Z   \n",
       "320040      10.0.0.15  stream:tcp  2022-09-19T20:33:23.439133Z         Z   \n",
       "320041      10.0.0.13  stream:tcp  2022-09-19T20:33:22.803383Z         Z   \n",
       "320042       10.0.0.8  stream:tcp  2022-09-19T20:33:22.386591Z         Z   \n",
       "320043  168.63.129.16  stream:tcp  2022-09-19T20:33:20.083213Z         Z   \n",
       "\n",
       "                                               @rawstring  \\\n",
       "4       {\"endtime\":\"2022-09-19T20:59:58.337959Z\",\"time...   \n",
       "5       {\"endtime\":\"2022-09-19T20:59:58.338787Z\",\"time...   \n",
       "6       {\"endtime\":\"2022-09-19T20:59:44.983553Z\",\"time...   \n",
       "7       {\"endtime\":\"2022-09-19T20:59:36.763847Z\",\"time...   \n",
       "8       {\"endtime\":\"2022-09-19T20:59:29.720741Z\",\"time...   \n",
       "...                                                   ...   \n",
       "320039  {\"endtime\":\"2022-09-19T20:33:23.588722Z\",\"time...   \n",
       "320040  {\"endtime\":\"2022-09-19T20:33:23.439133Z\",\"time...   \n",
       "320041  {\"endtime\":\"2022-09-19T20:33:22.803383Z\",\"time...   \n",
       "320042  {\"endtime\":\"2022-09-19T20:33:22.386591Z\",\"time...   \n",
       "320043  {\"endtime\":\"2022-09-19T20:33:20.083213Z\",\"time...   \n",
       "\n",
       "                                               @id  ...  http_content_length  \\\n",
       "4       02WG5MlfN9D7lTOJdOA4zjpz_54_361_1663621198  ...                  NaN   \n",
       "5       02WG5MlfN9D7lTOJdOA4zjpz_54_360_1663621198  ...                  NaN   \n",
       "6       02WG5MlfN9D7lTOJdOA4zjpz_54_359_1663621184  ...                  NaN   \n",
       "7       02WG5MlfN9D7lTOJdOA4zjpz_54_358_1663621176  ...                  NaN   \n",
       "8       02WG5MlfN9D7lTOJdOA4zjpz_54_357_1663621169  ...                  NaN   \n",
       "...                                            ...  ...                  ...   \n",
       "320039  nBCqAyE99Cb5cutkm4tiN8QR_1107_4_1663619603  ...                  NaN   \n",
       "320040  nBCqAyE99Cb5cutkm4tiN8QR_1107_3_1663619603  ...                  NaN   \n",
       "320041  nBCqAyE99Cb5cutkm4tiN8QR_1107_2_1663619602  ...                  NaN   \n",
       "320042  nBCqAyE99Cb5cutkm4tiN8QR_1107_1_1663619602  ...                  NaN   \n",
       "320043  nBCqAyE99Cb5cutkm4tiN8QR_1107_0_1663619600  ...                  NaN   \n",
       "\n",
       "        server uri_path http_method  status  form_data http_comment  \\\n",
       "4          NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "5          NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "6          NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "7          NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "8          NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "...        ...      ...         ...     ...        ...          ...   \n",
       "320039     NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "320040     NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "320041     NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "320042     NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "320043     NaN      NaN         NaN     NaN        NaN          NaN   \n",
       "\n",
       "       http_content_type uri_query http_user_agent  \n",
       "4                    NaN       NaN             NaN  \n",
       "5                    NaN       NaN             NaN  \n",
       "6                    NaN       NaN             NaN  \n",
       "7                    NaN       NaN             NaN  \n",
       "8                    NaN       NaN             NaN  \n",
       "...                  ...       ...             ...  \n",
       "320039               NaN       NaN             NaN  \n",
       "320040               NaN       NaN             NaN  \n",
       "320041               NaN       NaN             NaN  \n",
       "320042               NaN       NaN             NaN  \n",
       "320043               NaN       NaN             NaN  \n",
       "\n",
       "[167294 rows x 85 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['@sourcetype'] == 'stream:tcp') | (df['@sourcetype'] == 'stream:udp')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268465 records with count > 100\n"
     ]
    }
   ],
   "source": [
    "count_threshold = 100\n",
    "df = df.groupby(['src_ip', 'dest_ip', 'dest_port']).filter \\\n",
    "    (lambda x : len(x)>count_threshold)\n",
    "df = df.reset_index()\n",
    "print(len(df.index), \"records with count >\", count_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timestamp'] = pd.to_datetime(df['timestamp'], format='mixed')\n",
    "df['endtime'] = pd.to_datetime(df['endtime'], format='mixed')\n",
    "df['epoch_timestamp'] = df['timestamp'].astype('int64') // 10**9\n",
    "df['epoch_endtime'] = df['endtime'].astype('int64') // 10**9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>epoch_timestamp</th>\n",
       "      <th>endtime</th>\n",
       "      <th>epoch_endtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-09-19 14:15:22.282843+00:00</td>\n",
       "      <td>1663596922</td>\n",
       "      <td>2022-09-19 14:15:22.282843+00:00</td>\n",
       "      <td>1663596922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-09-19 13:35:52.852283+00:00</td>\n",
       "      <td>1663594552</td>\n",
       "      <td>2022-09-19 13:35:52.852283+00:00</td>\n",
       "      <td>1663594552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-09-19 20:59:58.337959+00:00</td>\n",
       "      <td>1663621198</td>\n",
       "      <td>2022-09-19 20:59:58.337959+00:00</td>\n",
       "      <td>1663621198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-09-19 20:59:58.338787+00:00</td>\n",
       "      <td>1663621198</td>\n",
       "      <td>2022-09-19 20:59:58.338787+00:00</td>\n",
       "      <td>1663621198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-09-19 20:59:44.867050+00:00</td>\n",
       "      <td>1663621184</td>\n",
       "      <td>2022-09-19 20:59:44.983553+00:00</td>\n",
       "      <td>1663621184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268460</th>\n",
       "      <td>2022-09-19 20:33:23.588685+00:00</td>\n",
       "      <td>1663619603</td>\n",
       "      <td>2022-09-19 20:33:23.588722+00:00</td>\n",
       "      <td>1663619603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268461</th>\n",
       "      <td>2022-09-19 20:33:23.275126+00:00</td>\n",
       "      <td>1663619603</td>\n",
       "      <td>2022-09-19 20:33:23.439133+00:00</td>\n",
       "      <td>1663619603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268462</th>\n",
       "      <td>2022-09-19 20:33:22.799918+00:00</td>\n",
       "      <td>1663619602</td>\n",
       "      <td>2022-09-19 20:33:22.803383+00:00</td>\n",
       "      <td>1663619602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268463</th>\n",
       "      <td>2022-09-19 20:33:22.228476+00:00</td>\n",
       "      <td>1663619602</td>\n",
       "      <td>2022-09-19 20:33:22.386591+00:00</td>\n",
       "      <td>1663619602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268464</th>\n",
       "      <td>2022-09-19 20:33:20.083176+00:00</td>\n",
       "      <td>1663619600</td>\n",
       "      <td>2022-09-19 20:33:20.083213+00:00</td>\n",
       "      <td>1663619600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268465 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              timestamp  epoch_timestamp  \\\n",
       "0      2022-09-19 14:15:22.282843+00:00       1663596922   \n",
       "1      2022-09-19 13:35:52.852283+00:00       1663594552   \n",
       "2      2022-09-19 20:59:58.337959+00:00       1663621198   \n",
       "3      2022-09-19 20:59:58.338787+00:00       1663621198   \n",
       "4      2022-09-19 20:59:44.867050+00:00       1663621184   \n",
       "...                                 ...              ...   \n",
       "268460 2022-09-19 20:33:23.588685+00:00       1663619603   \n",
       "268461 2022-09-19 20:33:23.275126+00:00       1663619603   \n",
       "268462 2022-09-19 20:33:22.799918+00:00       1663619602   \n",
       "268463 2022-09-19 20:33:22.228476+00:00       1663619602   \n",
       "268464 2022-09-19 20:33:20.083176+00:00       1663619600   \n",
       "\n",
       "                                endtime  epoch_endtime  \n",
       "0      2022-09-19 14:15:22.282843+00:00     1663596922  \n",
       "1      2022-09-19 13:35:52.852283+00:00     1663594552  \n",
       "2      2022-09-19 20:59:58.337959+00:00     1663621198  \n",
       "3      2022-09-19 20:59:58.338787+00:00     1663621198  \n",
       "4      2022-09-19 20:59:44.983553+00:00     1663621184  \n",
       "...                                 ...            ...  \n",
       "268460 2022-09-19 20:33:23.588722+00:00     1663619603  \n",
       "268461 2022-09-19 20:33:23.439133+00:00     1663619603  \n",
       "268462 2022-09-19 20:33:22.803383+00:00     1663619602  \n",
       "268463 2022-09-19 20:33:22.386591+00:00     1663619602  \n",
       "268464 2022-09-19 20:33:20.083213+00:00     1663619600  \n",
       "\n",
       "[268465 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['timestamp', 'epoch_timestamp', 'endtime', 'epoch_endtime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                 int64\n",
       "bytes_in              int64\n",
       "#type                object\n",
       "dest_mac             object\n",
       "#repo                object\n",
       "                      ...  \n",
       "http_content_type    object\n",
       "uri_query            object\n",
       "http_user_agent      object\n",
       "epoch_timestamp       int64\n",
       "epoch_endtime         int64\n",
       "Length: 88, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=['epoch_timestamp'], ascending=True)\n",
    "df['epoch_timestamp'] = df['epoch_timestamp'].astype(int)\n",
    "df['epoch_endtime'] = df['epoch_endtime'].astype(int)\n",
    "df['bytes'] = df['bytes'].astype(int)\n",
    "df['bytes_in'] = df['bytes_in'].astype(int)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time_diff_sec'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['epoch_timestamp'].transform(lambda x: x - x.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1</th>\n",
       "      <th>std1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>212167</th>\n",
       "      <td>5.847953e-03</td>\n",
       "      <td>0.076472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192002</th>\n",
       "      <td>5.847953e-03</td>\n",
       "      <td>0.076472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25321</th>\n",
       "      <td>5.847953e-03</td>\n",
       "      <td>0.076472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174854</th>\n",
       "      <td>5.847953e-03</td>\n",
       "      <td>0.076472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260500</th>\n",
       "      <td>5.847953e-03</td>\n",
       "      <td>0.076472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71516</th>\n",
       "      <td>1.160147e+06</td>\n",
       "      <td>1077.101422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71519</th>\n",
       "      <td>1.160147e+06</td>\n",
       "      <td>1077.101422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71512</th>\n",
       "      <td>1.160147e+06</td>\n",
       "      <td>1077.101422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71510</th>\n",
       "      <td>1.160147e+06</td>\n",
       "      <td>1077.101422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>1.160147e+06</td>\n",
       "      <td>1077.101422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268465 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                var1         std1\n",
       "212167  5.847953e-03     0.076472\n",
       "192002  5.847953e-03     0.076472\n",
       "25321   5.847953e-03     0.076472\n",
       "174854  5.847953e-03     0.076472\n",
       "260500  5.847953e-03     0.076472\n",
       "...              ...          ...\n",
       "71516   1.160147e+06  1077.101422\n",
       "71519   1.160147e+06  1077.101422\n",
       "71512   1.160147e+06  1077.101422\n",
       "71510   1.160147e+06  1077.101422\n",
       "8783    1.160147e+06  1077.101422\n",
       "\n",
       "[268465 rows x 2 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['std1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('std')\n",
    "df['var1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('var')\n",
    "df['count1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('count')\n",
    "df[['var1','std1']].sort_values(by=['std1'], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>src_ip</th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>dest_port</th>\n",
       "      <th>std1</th>\n",
       "      <th>var1</th>\n",
       "      <th>count1</th>\n",
       "      <th>app</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10005</th>\n",
       "      <td>10.0.0.16</td>\n",
       "      <td>20.7.2.167</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.076472</td>\n",
       "      <td>5.847953e-03</td>\n",
       "      <td>171</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10386</th>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>20.10.31.115</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.079809</td>\n",
       "      <td>6.369427e-03</td>\n",
       "      <td>157</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>20.7.2.167</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.138973</td>\n",
       "      <td>1.931348e-02</td>\n",
       "      <td>156</td>\n",
       "      <td>ssl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10711</th>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>20.7.1.246</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.152499</td>\n",
       "      <td>2.325581e-02</td>\n",
       "      <td>172</td>\n",
       "      <td>ssl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8646</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>10.0.15.255</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.324636</td>\n",
       "      <td>1.053885e-01</td>\n",
       "      <td>1371</td>\n",
       "      <td>dropbox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9196</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>162.125.2.14</td>\n",
       "      <td>443.0</td>\n",
       "      <td>318.094573</td>\n",
       "      <td>1.011842e+05</td>\n",
       "      <td>188</td>\n",
       "      <td>dropbox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>319.823560</td>\n",
       "      <td>1.022871e+05</td>\n",
       "      <td>147</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51892</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>13.107.4.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>343.434325</td>\n",
       "      <td>1.179471e+05</td>\n",
       "      <td>827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73010</th>\n",
       "      <td>10.0.0.9</td>\n",
       "      <td>13.107.4.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>976.185376</td>\n",
       "      <td>9.529379e+05</td>\n",
       "      <td>147</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71563</th>\n",
       "      <td>10.0.0.16</td>\n",
       "      <td>13.107.4.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1077.101422</td>\n",
       "      <td>1.160147e+06</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          src_ip       dest_ip  dest_port         std1          var1  count1  \\\n",
       "10005  10.0.0.16    20.7.2.167      443.0     0.076472  5.847953e-03     171   \n",
       "10386  10.0.0.13  20.10.31.115      443.0     0.079809  6.369427e-03     157   \n",
       "10707   10.0.0.4    20.7.2.167      443.0     0.138973  1.931348e-02     156   \n",
       "10711   10.0.0.8    20.7.1.246      443.0     0.152499  2.325581e-02     172   \n",
       "8646   10.0.0.18   10.0.15.255    17500.0     0.324636  1.053885e-01    1371   \n",
       "...          ...           ...        ...          ...           ...     ...   \n",
       "9196    10.0.0.4  162.125.2.14      443.0   318.094573  1.011842e+05     188   \n",
       "10754   10.0.0.4   40.87.160.0    23456.0   319.823560  1.022871e+05     147   \n",
       "51892   10.0.0.4   13.107.4.50       80.0   343.434325  1.179471e+05     827   \n",
       "73010   10.0.0.9   13.107.4.50       80.0   976.185376  9.529379e+05     147   \n",
       "71563  10.0.0.16   13.107.4.50       80.0  1077.101422  1.160147e+06     112   \n",
       "\n",
       "           app  \n",
       "10005  unknown  \n",
       "10386  unknown  \n",
       "10707      ssl  \n",
       "10711      ssl  \n",
       "8646   dropbox  \n",
       "...        ...  \n",
       "9196   dropbox  \n",
       "10754  unknown  \n",
       "51892      NaN  \n",
       "73010      NaN  \n",
       "71563      NaN  \n",
       "\n",
       "[75 rows x 7 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_df = df.drop_duplicates(['src_ip', 'dest_ip', 'dest_port'])\n",
    "unique_df[['src_ip', 'dest_ip', 'dest_port', 'std1', 'var1',\\\n",
    "    'count1', 'app']].sort_values(by=['std1'], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>src_ip</th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>dest_port</th>\n",
       "      <th>std1</th>\n",
       "      <th>var1</th>\n",
       "      <th>count1</th>\n",
       "      <th>app</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10386</th>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>20.10.31.115</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.079809</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>157</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>20.7.2.167</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.138973</td>\n",
       "      <td>0.019313</td>\n",
       "      <td>156</td>\n",
       "      <td>ssl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10711</th>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>20.7.1.246</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.152499</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>172</td>\n",
       "      <td>ssl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8646</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>10.0.15.255</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.324636</td>\n",
       "      <td>0.105388</td>\n",
       "      <td>1371</td>\n",
       "      <td>dropbox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8645</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>255.255.255.255</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.324636</td>\n",
       "      <td>0.105388</td>\n",
       "      <td>1371</td>\n",
       "      <td>dropbox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211097</th>\n",
       "      <td>52.151.54.66</td>\n",
       "      <td>10.0.0.12</td>\n",
       "      <td>3389.0</td>\n",
       "      <td>1.855641</td>\n",
       "      <td>3.443402</td>\n",
       "      <td>299</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10826</th>\n",
       "      <td>10.0.0.9</td>\n",
       "      <td>44.238.73.15</td>\n",
       "      <td>9997.0</td>\n",
       "      <td>4.323116</td>\n",
       "      <td>18.689332</td>\n",
       "      <td>1412</td>\n",
       "      <td>splunk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.012022</td>\n",
       "      <td>25.120366</td>\n",
       "      <td>13759</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>10.0.0.16</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.012544</td>\n",
       "      <td>25.125599</td>\n",
       "      <td>13759</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10800</th>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>10.0.0.9</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.012892</td>\n",
       "      <td>25.129088</td>\n",
       "      <td>13759</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.013936</td>\n",
       "      <td>25.139554</td>\n",
       "      <td>13759</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167652</th>\n",
       "      <td>20.106.221.251</td>\n",
       "      <td>10.0.0.15</td>\n",
       "      <td>3389.0</td>\n",
       "      <td>5.307421</td>\n",
       "      <td>28.168719</td>\n",
       "      <td>266</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189515</th>\n",
       "      <td>13.84.149.211</td>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>3389.0</td>\n",
       "      <td>5.533691</td>\n",
       "      <td>30.621740</td>\n",
       "      <td>103</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162085</th>\n",
       "      <td>52.151.52.236</td>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>3389.0</td>\n",
       "      <td>7.764796</td>\n",
       "      <td>60.292053</td>\n",
       "      <td>396</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8632</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>239.255.255.250</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>29.279678</td>\n",
       "      <td>857.299554</td>\n",
       "      <td>387</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>168.63.129.16</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.088509</td>\n",
       "      <td>3491.451936</td>\n",
       "      <td>834</td>\n",
       "      <td>windows_azure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10641</th>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>169.254.169.254</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.115566</td>\n",
       "      <td>8120.815176</td>\n",
       "      <td>459</td>\n",
       "      <td>windows_azure</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                src_ip          dest_ip  dest_port       std1         var1  \\\n",
       "10386        10.0.0.13     20.10.31.115      443.0   0.079809     0.006369   \n",
       "10707         10.0.0.4       20.7.2.167      443.0   0.138973     0.019313   \n",
       "10711         10.0.0.8       20.7.1.246      443.0   0.152499     0.023256   \n",
       "8646         10.0.0.18      10.0.15.255    17500.0   0.324636     0.105388   \n",
       "8645         10.0.0.18  255.255.255.255    17500.0   0.324636     0.105388   \n",
       "211097    52.151.54.66        10.0.0.12     3389.0   1.855641     3.443402   \n",
       "10826         10.0.0.9     44.238.73.15     9997.0   4.323116    18.689332   \n",
       "10812    168.63.129.16        10.0.0.18       80.0   5.012022    25.120366   \n",
       "10796    168.63.129.16        10.0.0.16       80.0   5.012544    25.125599   \n",
       "10800    168.63.129.16         10.0.0.9       80.0   5.012892    25.129088   \n",
       "10809    168.63.129.16         10.0.0.8       80.0   5.013936    25.139554   \n",
       "167652  20.106.221.251        10.0.0.15     3389.0   5.307421    28.168719   \n",
       "189515   13.84.149.211        10.0.0.13     3389.0   5.533691    30.621740   \n",
       "162085   52.151.52.236         10.0.0.4     3389.0   7.764796    60.292053   \n",
       "8632         10.0.0.18  239.255.255.250     1900.0  29.279678   857.299554   \n",
       "8617          10.0.0.4    168.63.129.16       53.0  59.088509  3491.451936   \n",
       "10641         10.0.0.8  169.254.169.254       80.0  90.115566  8120.815176   \n",
       "\n",
       "        count1            app  \n",
       "10386      157        unknown  \n",
       "10707      156            ssl  \n",
       "10711      172            ssl  \n",
       "8646      1371        dropbox  \n",
       "8645      1371        dropbox  \n",
       "211097     299        unknown  \n",
       "10826     1412         splunk  \n",
       "10812    13759        unknown  \n",
       "10796    13759        unknown  \n",
       "10800    13759        unknown  \n",
       "10809    13759        unknown  \n",
       "167652     266        unknown  \n",
       "189515     103        unknown  \n",
       "162085     396        unknown  \n",
       "8632       387            udp  \n",
       "8617       834  windows_azure  \n",
       "10641      459  windows_azure  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_threshold = 100\n",
    "unique_df = unique_df.loc[unique_df['std1'] <  \\\n",
    "    std_threshold].sort_values(by=['dest_ip'], ascending=True)\n",
    "unique_df[['src_ip', 'dest_ip', 'dest_port', \\\n",
    "    'std1', 'var1', 'count1',  'app']].sort_values(by=['std1'], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dest_ip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>211097</th>\n",
       "      <td>10.0.0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189515</th>\n",
       "      <td>10.0.0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167652</th>\n",
       "      <td>10.0.0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>10.0.0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>10.0.0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162085</th>\n",
       "      <td>10.0.0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>10.0.0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10800</th>\n",
       "      <td>10.0.0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8646</th>\n",
       "      <td>10.0.15.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>168.63.129.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10641</th>\n",
       "      <td>169.254.169.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10386</th>\n",
       "      <td>20.10.31.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10711</th>\n",
       "      <td>20.7.1.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>20.7.2.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8632</th>\n",
       "      <td>239.255.255.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8645</th>\n",
       "      <td>255.255.255.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10826</th>\n",
       "      <td>44.238.73.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dest_ip\n",
       "211097        10.0.0.12\n",
       "189515        10.0.0.13\n",
       "167652        10.0.0.15\n",
       "10796         10.0.0.16\n",
       "10812         10.0.0.18\n",
       "162085         10.0.0.4\n",
       "10809          10.0.0.8\n",
       "10800          10.0.0.9\n",
       "8646        10.0.15.255\n",
       "8617      168.63.129.16\n",
       "10641   169.254.169.254\n",
       "10386      20.10.31.115\n",
       "10711        20.7.1.246\n",
       "10707        20.7.2.167\n",
       "8632    239.255.255.250\n",
       "8645    255.255.255.255\n",
       "10826      44.238.73.15"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_df = unique_df.loc[unique_df['std1'] < \\\n",
    "    std_threshold].drop_duplicates(['dest_ip'])\n",
    "unique_df[['dest_ip']].sort_values(by=['dest_ip'], \\\n",
    "    ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.2 Jupyter notebook code — Exclude benign traffic by destination IP address "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>src_ip</th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>dest_port</th>\n",
       "      <th>std1</th>\n",
       "      <th>var1</th>\n",
       "      <th>count1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [src_ip, dest_ip, dest_port, std1, var1, count1]\n",
       "Index: []"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_df.loc[\n",
    "   (unique_df['src_ip'].str.startswith('10.')) \\\n",
    "    & (unique_df['dest_port'] != 9997) \\\n",
    "    & (~unique_df['dest_ip'].str.endswith(\".255\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"20.7.1\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"20.7.2\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"20.10.31.115\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"168.63.129.16\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"169.254.169.254\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"239.255.255.250\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"13.107.4.50\")) \\\n",
    "    , ['src_ip', 'dest_ip', 'dest_port', 'std1', 'var1', 'count1']\n",
    "    ].sort_values(by=['std1'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enhancing the analytic techniques with interquartile range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.3 Jupyter Notebook code — Calculate the lower and upper quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320044\n",
      "268465 records with count > 100\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_original = pd.read_json(\"ch7_stream_events.json\")\n",
    "print(len(df_original))\n",
    "df = df_original\n",
    "\n",
    "count_threshold = 100\n",
    "df = df.groupby(['src_ip', 'dest_ip', 'dest_port']).filter \\\n",
    "    (lambda x : len(x)>count_threshold)\n",
    "df = df.reset_index()\n",
    "print(len(df.index), \"records with count >\", count_threshold)\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'], format='mixed')\n",
    "df['endtime'] = pd.to_datetime(df['endtime'], format='mixed')\n",
    "df['epoch_timestamp'] = df['timestamp'].astype('int64') // 10**9\n",
    "df['epoch_endtime'] = df['endtime'].astype('int64') // 10**9\n",
    "\n",
    "df = df.sort_values(by=['epoch_timestamp'], ascending=True)\n",
    "df['epoch_timestamp'] = df['epoch_timestamp'].astype(int)\n",
    "df['epoch_endtime'] = df['epoch_endtime'].astype(int)\n",
    "df['bytes'] = df['bytes'].astype(int)\n",
    "df['bytes_in'] = df['bytes_in'].astype(int)\n",
    "df.dtypes\n",
    "\n",
    "df['time_diff_sec'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['epoch_timestamp'].transform(lambda x: x - x.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "53d1fe7a-4de3-4569-9185-44c853890b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lower_quartile'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform(lambda x: x.quantile(q=0.25))\n",
    "df['upper_quartile'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform(lambda x: x.quantile(q=0.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.4 Jupyter notebook code — Drop rows with values of time_diff_sec outside the IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "31c7d462-46a5-410c-bdbc-0c40260b050a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df[(df['time_diff_sec'] < df['lower_quartile'])].index)\n",
    "df = df.drop(df[(df['time_diff_sec'] > df['upper_quartile'])].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.5 Jupyter notebook code — Calculate standard deviation, variance, and number of connections per src_ip, dest_ip and dest_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "98395983-9ae6-444a-9d87-0d88eb9a455e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['std1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('std')\n",
    "df['var1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('var')\n",
    "df['count1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.6 Jupyter notebook code — Drop duplicate rows based on src_ip,dest_ip, and dest_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a6039521-2f58-4030-95fd-a205fcd939f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_df = df.drop_duplicates(['src_ip', 'dest_ip', 'dest_port'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.7 Jupyter notebook code — Keep rows with low standard deviation values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c33ec6ab-3e41-493b-b37e-df51770fd4a7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>std1</th>\n",
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       "      <th>app</th>\n",
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       "      <td>9997.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1235</td>\n",
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       "    <tr>\n",
       "      <th>10711</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>10707</th>\n",
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       "      <td>20.7.2.167</td>\n",
       "      <td>443.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>153</td>\n",
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       "      <th>10287</th>\n",
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       "      <td>443.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>13.107.4.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>696</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <td>10.0.0.12</td>\n",
       "      <td>169.254.169.254</td>\n",
       "      <td>80.0</td>\n",
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       "      <td>8114.767184</td>\n",
       "      <td>451</td>\n",
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       "    <tr>\n",
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       "      <td>10.0.0.16</td>\n",
       "      <td>169.254.169.254</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.082288</td>\n",
       "      <td>8114.818645</td>\n",
       "      <td>449</td>\n",
       "      <td>windows_azure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72853</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>169.254.169.254</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.083446</td>\n",
       "      <td>8115.027231</td>\n",
       "      <td>452</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>72647</th>\n",
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       "      <td>169.254.169.254</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.083755</td>\n",
       "      <td>8115.082846</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>10754</th>\n",
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       "      <td>76</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          src_ip          dest_ip  dest_port       std1         var1  count1  \\\n",
       "10765  10.0.0.18     44.238.73.15     9997.0   0.000000     0.000000    1235   \n",
       "10711   10.0.0.8       20.7.1.246      443.0   0.000000     0.000000     171   \n",
       "10707   10.0.0.4       20.7.2.167      443.0   0.000000     0.000000     153   \n",
       "10287   10.0.0.9       20.7.2.167      443.0   0.000000     0.000000     171   \n",
       "51892   10.0.0.4      13.107.4.50       80.0   0.000000     0.000000     696   \n",
       "...          ...              ...        ...        ...          ...     ...   \n",
       "72905  10.0.0.12  169.254.169.254       80.0  90.082003  8114.767184     451   \n",
       "10215  10.0.0.16  169.254.169.254       80.0  90.082288  8114.818645     449   \n",
       "72853  10.0.0.18  169.254.169.254       80.0  90.083446  8115.027231     452   \n",
       "72647   10.0.0.9  169.254.169.254       80.0  90.083755  8115.082846     450   \n",
       "10754   10.0.0.4      40.87.160.0    23456.0  97.601937  9526.138070      76   \n",
       "\n",
       "                 app  \n",
       "10765         splunk  \n",
       "10711            ssl  \n",
       "10707            ssl  \n",
       "10287            ssl  \n",
       "51892            NaN  \n",
       "...              ...  \n",
       "72905            NaN  \n",
       "10215  windows_azure  \n",
       "72853            NaN  \n",
       "72647            NaN  \n",
       "10754        unknown  \n",
       "\n",
       "[75 rows x 7 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_threshold = 100\n",
    "unique_df = unique_df.loc[unique_df['std1'] < std_threshold].sort_values(by=['dest_ip'], ascending=True)\n",
    "unique_df[['src_ip', 'dest_ip', 'dest_port', 'std1', 'var1', 'count1', 'app']].sort_values(by=['std1'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.8 Jupyter notebook code — Exclude benign traffic by destination IP address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8e6d43e4-001b-4d07-84b2-e04e5ffa19a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>10306</th>\n",
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       "      <td>72.778727</td>\n",
       "      <td>5296.743137</td>\n",
       "      <td>85</td>\n",
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       "      <th>10515</th>\n",
       "      <td>10.0.0.12</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
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       "      <td>87.424252</td>\n",
       "      <td>7642.999849</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10634</th>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>88.084637</td>\n",
       "      <td>7758.903297</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>97.601937</td>\n",
       "      <td>9526.138070</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          src_ip       dest_ip  dest_port       std1         var1  count1\n",
       "9776   10.0.0.18  162.125.2.14      443.0  28.005122   784.286854     119\n",
       "9196    10.0.0.4  162.125.2.14      443.0  56.820850  3228.609014      94\n",
       "10464  10.0.0.15   40.87.160.0    23456.0  60.947747  3714.627879     100\n",
       "10004  10.0.0.18   40.87.160.0    23456.0  65.965584  4351.458242     105\n",
       "10306   10.0.0.9   40.87.160.0    23456.0  72.778727  5296.743137      85\n",
       "10515  10.0.0.12   40.87.160.0    23456.0  77.754161  6045.709502     108\n",
       "10753   10.0.0.8   40.87.160.0    23456.0  78.296745  6130.380235      84\n",
       "9660   10.0.0.16   40.87.160.0    23456.0  87.424252  7642.999849      82\n",
       "10634  10.0.0.13   40.87.160.0    23456.0  88.084637  7758.903297      91\n",
       "10754   10.0.0.4   40.87.160.0    23456.0  97.601937  9526.138070      76"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_df.loc[\n",
    "   (unique_df['src_ip'].str.startswith('10.')) \\\n",
    "    & (unique_df['dest_port'] != 9997) \\\n",
    "    & (~unique_df['dest_ip'].str.endswith(\".255\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"20.7.1\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"20.7.2\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"20.10.31.115\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"168.63.129.16\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"169.254.169.254\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"239.255.255.250\")) \\\n",
    "    & (~unique_df['dest_ip'].str.contains(\"13.107.4.50\")) \\\n",
    "    , ['src_ip', 'dest_ip', 'dest_port', 'std1', 'var1', 'count1']\n",
    "    ].sort_values(by=['std1'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interrogating the first suspect, 162.125.2.14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.10 Jupyter notebook code — Retrieve details about connections between 10.0.0.18 and 162.125.2.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "00a45888-e8f9-4951-9749-c89648d36223",
   "metadata": {},
   "outputs": [
    {
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       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>784.286854</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         src_ip       dest_ip  dest_port      app       std1  lower_quartile  \\\n",
       "9776  10.0.0.18  162.125.2.14      443.0  dropbox  28.005122            50.0   \n",
       "\n",
       "      upper_quartile        var1  count1  \n",
       "9776           156.0  784.286854     119  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_df.loc[(unique_df['src_ip'] == '10.0.0.18') & \\\n",
    "    (unique_df['dest_ip'] == '162.125.2.14') & \\\n",
    "        (unique_df['dest_port'] == 443), ['src_ip', \\\n",
    "            'dest_ip', 'dest_port', 'app', 'std1', 'lower_quartile',\n",
    "                'upper_quartile','var1', 'count1']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.12 Jupyter notebook code — Retrieve details about connections between 10.0.0.4 and 162.125.2.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>src_ip</th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>dest_port</th>\n",
       "      <th>app</th>\n",
       "      <th>std1</th>\n",
       "      <th>lower_quartile</th>\n",
       "      <th>upper_quartile</th>\n",
       "      <th>var1</th>\n",
       "      <th>count1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9196</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>162.125.2.14</td>\n",
       "      <td>443.0</td>\n",
       "      <td>dropbox</td>\n",
       "      <td>56.82085</td>\n",
       "      <td>46.75</td>\n",
       "      <td>266.25</td>\n",
       "      <td>3228.609014</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        src_ip       dest_ip  dest_port      app      std1  lower_quartile  \\\n",
       "9196  10.0.0.4  162.125.2.14      443.0  dropbox  56.82085           46.75   \n",
       "\n",
       "      upper_quartile         var1  count1  \n",
       "9196          266.25  3228.609014      94  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_df.loc[(unique_df['src_ip'] == '10.0.0.4') & \\\n",
    "    (unique_df['dest_ip'] == '162.125.2.14') & \\\n",
    "        (unique_df['dest_port'] == 443), ['src_ip', \\\n",
    "            'dest_ip', 'dest_port', 'app', 'std1', 'lower_quartile',\n",
    "                'upper_quartile','var1', 'count1']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7e14d9-8ce6-45d9-8703-b3ee804ca246",
   "metadata": {},
   "source": [
    "## Analyzing the data further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.24 Jupyter notebook code — Retrieve and process the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320044\n",
      "268465 records with count > 100\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_original = pd.read_json(\"ch7_stream_events.json\")\n",
    "print(len(df_original))\n",
    "df = df_original\n",
    "\n",
    "count_threshold = 100\n",
    "df = df.groupby(['src_ip', 'dest_ip', 'dest_port']).filter \\\n",
    "    (lambda x : len(x)>count_threshold)\n",
    "df = df.reset_index()\n",
    "print(len(df.index), \"records with count >\", count_threshold)\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'], format='mixed')\n",
    "df['endtime'] = pd.to_datetime(df['endtime'], format='mixed')\n",
    "df['epoch_timestamp'] = df['timestamp'].astype('int64') // 10**9\n",
    "df['epoch_endtime'] = df['endtime'].astype('int64') // 10**9\n",
    "\n",
    "df = df.sort_values(by=['epoch_timestamp'], ascending=True)\n",
    "df['epoch_timestamp'] = df['epoch_timestamp'].astype(int)\n",
    "df['epoch_endtime'] = df['epoch_endtime'].astype(int)\n",
    "df['bytes'] = df['bytes'].astype(int)\n",
    "df['bytes_in'] = df['bytes_in'].astype(int)\n",
    "df.dtypes\n",
    "\n",
    "df['time_diff_sec'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['epoch_timestamp'].transform(lambda x: x - x.shift(1))\n",
    "\n",
    "df['lower_quartile'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform(lambda x: x.quantile(q=0.25))\n",
    "df['upper_quartile'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform(lambda x: x.quantile(q=0.75))\n",
    "\n",
    "df['std1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('std')\n",
    "df['var1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('var')\n",
    "df['count1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.25 Jupyter notebook code — Plot the value of time_diff_sec for connections between 10.0.0.4 and 162.125.2.14 over port 443"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "384451c4-5c25-4c40-9041-916ccef05863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='epoch_timestamp'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.4') &(df['dest_ip'] == '162.125.2.14') & \\\n",
    "       (df['dest_port'] == 443)].sort_values(by=['epoch_timestamp'], \\\n",
    "              ascending = True).set_index('epoch_timestamp')\\\n",
    "                     ['time_diff_sec'].plot(figsize=[25,5], \\\n",
    "                            kind = 'line', color = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.26 Jupyter notebook code — Plot the value of time_diff_sec for connections between 10.0.0.18 and 162.125.2.14 over port 443"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "251ba901-1408-4b10-8896-08c44390fca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='epoch_timestamp'>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.18') &(df['dest_ip'] == '162.125.2.14') & \\\n",
    "       (df['dest_port'] == 443)].sort_values(by=['epoch_timestamp'], \\\n",
    "              ascending = True).set_index('epoch_timestamp')\\\n",
    "                     ['time_diff_sec'].plot(figsize=[25,5], \\\n",
    "                            kind = 'line', color = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.27 Jupyter notebook code — Plot the values of time_diff_sec for connections between 10.0.0.4 and 162.125.2.14 over port 443"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "0a394b41-df59-47bf-b8f9-148e1148244d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.4') &(df['dest_ip'] == '162.125.2.14') & \\\n",
    "    (df['dest_port'] == 443)].sort_values(by=['epoch_timestamp'], \\\n",
    "        ascending = True).set_index('epoch_timestamp')\\\n",
    "            ['time_diff_sec'].hist(figsize = [25,5], bins = 50, \\\n",
    "                color = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.28 Jupyter notebook code — Plot the values of time_diff_sec for connections between 10.0.0.18 and 162.125.2.14 over port 443"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.18') &(df['dest_ip'] == '162.125.2.14') & \\\n",
    "    (df['dest_port'] == 443)].sort_values(by=['epoch_timestamp'], \\\n",
    "        ascending = True).set_index('epoch_timestamp')\\\n",
    "            ['time_diff_sec'].hist(figsize = [25,5], bins = 50, \\\n",
    "                color = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing fields of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.30 Jupyter notebook code – Show the distribution field bytes for connections between 10.0.0.4 and IP addresses that contain 162.125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y7iXqfsxxbOICIGxQ9oB6YDuOduaV6O7PV2jymt2m4wBA2Nm3N0sTX3hSS3/4RiOvvkGNW7aWxHo+AKGNsgfUIX/AVsBx9OzkDXp12kaV+pmyCQAmZa5fo7f/fpP6nHCKhp5/iXwRkYzyAQhZlD2gDhyYsjlp9S498NVq7cjlUHQACBaObWvxxC+1bs4MHXPxFZzPByBkUfaAWmY7jrblFOmfn67QjA1ZpuMAAP5AYW6Ovnn2cS378TuNvOp6NWzWnGmdAEIKZQ+oJf6ALb/t6Mkf1umNmZtUHuAsBQBwg+2rluvNv96g/qecpqPOvlAer5dRPgAhgbIH1NCBKZtfL8/UQ9+s1q78UtORAABVZAf8mv/FBK2ZOVXHXnq1Og46iqmdAFyPsgfUQMDev8vmXz9eqtnpe03HAQDU0L69WfriyYfUpnc/HX/l9UponMKB7ABci2cvoBoC9v5dNd+avVnHPzmVogcAIWbz0kUad+t1WvjVp3JsW3YgYDoSAFQZI3tAFQVsRxm5Jbr1wyWavznHdBwAQB3xl5dp2vg3tG7uTJ10w21q2LSZLEb5ALgIz1hAJfltW7bj6I2ZmzTyqakUPQAIEzs3rNNbt9+geZ9/LJtRPgAuwsgeUAkB29GOnGKN/WCpFm2l5AFAuAmUl2vG+29p3dyZOvnGvyq5WXNG+QAEPZ6lgEPwB2zZtqNXp2/UCU9No+gBQJjbvSldb91+o+Z88r7sQIBRPgBBjZE94A/YtqOt2UW69cOlWrIt13QcAECQsAN+zfroXa2fN1sn33CbGrVszWHsAIISI3vArxwYzXtparpO+u90ih4A4Hft2bJJb99xs2Z9NJ5RPgBBiZE94Bds29HmvUUa+8ESLd+RZzoOACDI2YGA5kx4XxvmzdbJN92uxi1asZYPQNDg2QjQ/g1YbNvR81M26OT/TqfoAQCqJGvbFo2/8xYt+OpTOY7DKB+AoMDIHsKeP2Arp6hc17+7SPM2ZZuOAwBwqYDfr2nj39CmJQt1yk23KyYhUR6v13QsAGGMkT2Evanr9mjkU1MpegCAWrFt5TKNu/U6bZg/R5LkOI7hRADCFWUPYckfsOUP2PrXlyt1xZsLlFtUbjoSACCElBQW6MunHtbEF56Sv7xMdsBvOhKAMMQ0ToSdgO0oM69E141fqBU78k3HAQCEsJVTf1TGutU6deydatyyNZu3AKhXPOMgbByYRvPl0gyd+PQ0ih4AoF7kZGZo/D/GauE3X0iSbNs2nAhAuGBkD2HBH7Dltx3d9elyTVi0w3QcAECYCfj9mvr2a9q6fIlOvvE2RcbEsnkLgDrHyB5CXsB2tDGrUKc8M52iBwAwatOSBRp321+0Y+0qNm4BUOcoewhZ9s+/RMfP3aJTn52h9D2FhhMBACAV5uboowfu0vzPP5YkOUzrBFBHmMaJkOQP2Copt3XbR0v03cpdpuMAAHAQx7Y1/b03lblhnU6+4TZ5I3zyeHlZBqB2MbKHkGPbjpbvyNOop6dR9AAAQW3D/Nl6+46blLszU3YgYDoOgBBD2UPIOLD24d15W3XOy7O1I7fYcCIAAA4vJzND79w5VuvmzjQdBUCIoewhJARsW7Yj3fXpcv3zsxUqD7DoHQDgHuWlJfr6v49q8hsvyw4EZNuM8gGoOSaHw/X8AVsFpX5d9dYCzd+cYzoOAADVtnjil9q9KV2j/3qXouPiOZ4BQI0wsgdXC9iO1u8u0Mn/nU7RAwCEhB1rV+mt229Q5oa17NQJoEYoe3C1b5ZnaswLM5WRV2I6CgAAtaYwN0cf/utOLfzmC0niTD4A1ULZg+vYtiPbcfTIt6t143uLVVLOu54AgNBjBwKa+vZr+ubZx1nHB6BaWLMHV/EHbJX6bd3w7mL9tHa36TgAANS51TOmKG/PLo352z2KiImRl/P4AFQSI3twDb9ta0dusUY/N4OiBwAIKxlrV+udO29R3q6dnMcHoNIoe3AFx3E0c32WTn12htL3FJqOAwBAvcvbvUvj/3Grtq1czsYtACqFsoegZv+8IP2lqem6bNx85Zf4DScCAMCcsuIiTXj4Hi2dNNF0FAAuwKRvBK3Azxux3PLhEn2xNMN0HAAAgoJj2/rx9ReUvWOrjr30akmS5eH9ewC/RdlDUPIHbJWU27ps3DzOzwMA4HcsnviVcnfu1Klj75A3IoID2AH8Bm8DIej4A7Zyisp1xoszKXoAABzCpiUL9O4/b1Nhbg4btwD4Dcoegoo/YGtrdpFGPzdD63YVmI4DAEDQy9q2Re/ceYt2b0rnLD4AB6HsIWgEbEdLt+dpzAuzlJlXYjoOAACuUZSXqw/+dac2L1nETp0AKlD2EBRsx9HkNbt0watzlFdcbjoOAACu4y8r1eeP/1urpv9kOgqAIEHZQ1B4b95WXfP2QpX6eTcSAIDqsgMBTXzhKc3/YoLpKACCAGUPxjg/n6H3xPdrddenK2Q7hgMBABAipo1/Q1Pf+Z+k//99CyD8cPQCjLAdR3KkOz5Zpg/mbzMdBwCAkLPgy09UvC9fJ1xzk+Q4nMUHhCHKHupdwHYUsB39ZfxCTVq923QcAABC1sopk1SyL1+njr1TluNwFh8QZniLB/XKH7BVWOrXea/MoegBAFAP0hfO00f//qf8ZWWcxQeEGcoe6o0/YCuroExjXpilRVs5LB0AgPqyY81KvXfP7SopLFAg4DcdB0A9oeyhXvgDtrbnFuu052cofQ+HpQMAUN+ytm7W+H/cqoK9exnhA8IEZQ91zh+wtWVvkc56cZZ25ZeajgMAQNjK37NL7/7zNuXuzKTwAWGAsoc65Q/YSt9TqLNfnq2sgjLTcQAACHtFebl6/76/KydzB4UPCHGUPdQZv21r7a59Oufl2coupOgBABAsivPz9MF9dyg7YzuFDwhhlD3UCb9ta2VGvs5/ZY7yistNxwEAAL9SvC9fH9x3h/bu2EbhA0IUZQ+1LmA7WrYtTxe+Olf5Jez4BQBAsCop2KcP77tDWdu3UviAEETZQ60K2I4Wb83RRa/PVUEpRQ8AgGBXUligD/91h7K2babwASGGsodaE7BtLdmWq0v+N09FZfyyAADALUoLC/Xhv/6hPVs2UfiAEOIzHQChIWA7WrotT5e8PpeiB7hQbKRXcZE+xUV5FRflU0yEV16PJY9lybIkr8dS19RESVJap66KSUiUHEeO48i2bflLS1VeVip/aYnKS0vlLytVeWmpHNs2fM8AVFZpUaE+vP8fOvvuB9WkTTt5vF7TkQDUkOU4jmM6BNzNb9tavj1PF702V4UUPcCopNgINUmIUpOEaDVN3P/fJolRahQXqbhon5KiI9QgJmJ/oYv0KibCqwifRx7LqtTt27Ytj6fyk0LsQECB8nL5/eUqLSpUUW6u9mVnqTgvV0X5efv/5OWpKD9XxXn7/15SWCDxqwkwJjImRmfd9W81bdeBwge4HGUPNeK3ba3cka8LX2ONHlDX4qN8atM4Vu0ax6tlcqyaJESpaWK0WibHqGlCtJJiI+TzHlzEfv0Ub1Wy1NWlw2Wy7YDKiopUlJ+nguy9Ksjeq5zMDGVnbNPeHduVm7lDAT/PN0BdioiO0Vl3PaDU9h0pfICLUfZQbX7b1qqMfF346lzto+gBtSLCa6lVcpzapcSpbeP9fzo1jVeHJglqEBNRcb1fPnUHQ4GrSwfu64H76di2CvJylLVlk7K2blH2ju3Kztim7B3b948KAqgVkTGxOve+R9S4ZWsKH+BSlD1Uiz+w/8D0816eQ9EDqqllcox6pDVQz+YN1CMtUZ1TE5WSECWP5+dS86uSg9/69feotKhIuTsztHPTBmVv3/pzEdyu/Kw9TA0FqiEmIVHnP/C4GjRpSuEDXIiyhyrzB2ztyC3WmBdmKbuwzHQcwBVaJceqZ/P9xa5Pqwbq2TxJcVH798ii1NW+X39P/f5y5WZmavfmdGWsXaXta1Zp7/atFECgEuKTG+mCfz+huKQkebzs7Qe4CWUPVeIP2MotKtfpL8zU9pxi03GAoJSSEKWBbZLVs0UD9W2ZpJ4tGig2kmIXDH75/S8rKdb2VSu1bdUy7VizUrs2bmDLeeAPJKWm6YIHHlNUXDwjfICLUPZQaQHbUXFZQGe8OFPrdrEuBjggJSFKR7ZN1pHtGml45xQ1bxgriWLnBr/8Nwr4/dq1KV1bli7U9jUrlbFujfylpYYTAsGjcas2Ou/+RxURGUXhA1yCsodKsW1HftvR+a/O0cItOabjAEYdqtxR7NzvwL+jbdvK3r5Vm5cu0vY1K7VjzSqVFOwzHQ8wqlnHLjrnnofk8fmqdAwLADMoezgsx3FkO9JVby3Q5DW7TccB6l1ClE9Hd2qsIe0b65jOKWpBuQsrv/x3zt29U1uWLtb2NSu1dfkSFeXlmg0HGNC6Zx+NueM+eTweWRQ+IKhR9lApt364RJ8s2mE6BlBvWjSM0fFdm2pU96Ya2CZZXq+HcgdJ/1/+HMfRzg3rtGbWNG2YP1v5e3gzDOGjw4DBGn3rnZJl8bwIBDHKHg7r31+v0mvTN5mOAdQpy5L6tEjSiK5NdUqvZmrbOI41dzisXz5GsrZv1erpP2nDvNnKzthuOBlQ97oNO04nXX+r6RgADoGyh0N6aUq6Hpm4xnQMoE7ERHh1dMfGGtG1qU7qkarEmAgKHqrtl4+dvN27tWr6ZK2fO1N7tvBmGUJX35NG67g/X206BoA/QNnD77IdRxMWbtftHy8zHQWoVZFej4Z3TtGYvs11fLemimB6JurIgcdVYV6u1kz/SWvnzFTmhrWc7YeQc8xFl6v/n8bwPAoEIcoefiNgO/pp7W5d8/ZCBWweHnA/y5IGtknWaX2a67Q+aYqL8lHwUK8OPN6KC/ZpzYypWj9vlravXiHHtk1HA2rOsjR67J1qP2CQPB6OZACCCWUPB/HbtpZuy9MFr85RqZ8XIXC3zk0TdHrfNJ19REs1jo+i4CEoHHgclhYVat2cmVo7Z4a2LFvMiB9czRcRqXPve0RN2rbnDD4giFD2UMEfsLUpq1BnvjRL+cV+03GAamnWIFqn9UnTOUe0VLuUeAoegtqBx+e+vVlaPPFLrfjpBxXvyzcdC6iWmMQGuujhpxXfMJnCBwQJyh4k7S96OUXlOuWZ6dq9r9R0HKBKvB5Lx3dtoj8f1UaD2zdmkxW4zoHHrGPbWjd3ppZM/Eo71q4ynAqouuS0FrrgwScUER3NlE4gCFD2INtxVB6wdeaLs7RiB+8owz2aJETpvIEt9eej2io5LpJRPISEA4/j3F2ZWvDVp1o9/SeVFRebjgVUWouuPXT23Q/K8nh4TgYMo+xBknT9+EX6enmm6RhApQxu30iXHtlaJ3RPlWUxgofQ5TiOAn6/Vk79UUu//5pjHOAaXYYO1yk3/tV0DCDsUfag/05ap6cmrTcdAzikxGifzuzfQpcPaauWybGM4iGsHHi879qUroVff6Z1c2YoUF5uOhZwSEeecZ6GnHuR6RhAWKPshbGA7eiHVTt13fhFbAKHoNUlNUGXDWmjMf2aK8LjkcRIHsLXgdJXVlyspT98o6WTvlXerp2mYwF/aNR1t6j7sONk/fz8DaB+UfbClD9ga8OeAo15fpaKywOm4wC/MaBNQ914XAcN69SEUTzgdxz4udi6fKkWT/xS6YvmcW4fgo7H69NZd92v5l26s0MnYABlLwwFbEd5xeX60zPTlZFXYjoOUMGypOO6NNHNx3VUr5ZJlDygEg78nOTt2a1ZH76j1TOmUPoQVKJi43Txf55RQqPGFD6gnlH2wozjOPLbjs59ebYWbc01HQeQJPk8lv7UK003H99RbRvHUfKAavhl6Zv5/ltaM3OaHIfSh+DQqEUrXfjwU/L5IpjSCdQjyl4Yuu3DJZqwaIfpGICiIzw654iWuuHYDmqSGE3JA2rB/5e+XZrx3ltaO2s6pQ9BoePAozT6tn+YjgGEFcpeGHEcRy9P26hHvl1jOgrCXEKUT5cc1UbXDGunhGifJDZdAWrbL0vf9PHjtHbODLEbF0wbcu7FGjTmHJ7zgXpC2QsTftvW9HVZuuLN+bL5F4chUT6PLhncRreM6KjYKC+/7IF6lLd7t6aNf13r5s6i9MEYy/LojDvuVauefVi/B9QDyl4Y8Adsbc0u0ujnZqqg1G86DsKQ12PprP4t9PcTu6hhbAQlDzAod2eGpo0fp/XzZ1P6YERUXJwufoQNW4D6QNkLcQHbUWGpX6c8O13bsotNx0GYsSzppB6p+sfJXdWiIQehA8HgwM9hdsZ2TX/3TW2YP9t0JIShRi1b68KHnmTDFqCOUfZCnG07uvC1uZq9ca/pKAgzR3dsrLtO7qouzRIpeUAQOvBzmbVti2a896bSF84zHQlhhg1bgLpH2QthjuPose/W6oUp6aajIIz0aZmkf5zcRQPbNqLkAS5w4Od095ZNmjb+DW1Zush0JISRoeddooGnn83vCqCOUPZClD9ga87Gvbr4f/NYkoF60TI5Rnef3E0n9Eil5AEudODnNn3hPE1+42Xl79llOhLCABu2AHWLsheCAratnKJyjXpqmvYWlpmOgxAX5fPoL8Pb6/pjO8jrsSh5gMs5jiM7ENDcTz/Q/M8nyF/O7xHULTZsAeoOZS8E2baj81+do7mbsk1HQYg7oVtTPXB6DzVJiKLkASHGcRwVZGdr0mvPa+Mi1vOhbjVp214X/PsJeX0+01GAkELZCzGO4+iJH9bpuckbTEdBCGvXOE4PnN5dQzqkMGUTCGEHfr43LVmoH//3ovJ27TQdCSGs74mn6rjLrjEdAwgplL0Q4g/YmrcpWxe9PpeD01EnYiO9uvG4Drr66HbyMGUTCBuO48i2bc379EPN+/xj+ctKTUdCiDr99rvVtu8RTOcEagllL0QEbEe5RWUa9fQ0ZRWwvgK170+9mun+0T3UMI5D0YFw5TiOCnNz9OPrL3I+H+pEdFy8Ln3iecUmJlH4gFpA2QsRtuPowlc5Tw+1r13jOP3nrJ4a0IajFAD8/9TOLcuX6MfXX1BOZobpSAgxaZ276tz7/iMPh60DNUbZCwGO4+jpSev13x/Xm46CEOKxpCuPbqe/jerMLpsAfuPA1M7ZH43X/C8myA4ETEdCCBl4+tkaet4l/O4Baoiy53L+gK0FW3J0watzWKeHWtOhSbyeObePuqYl8osWwCE5jqOsbVv0zbOPK2vrZtNxECIsy6Oz7v63WnTpznROoAYoey4WsB3lFe8/T29PAYvlUXNej6VrhrXTrSM7MZoHoNIcx5Fj25rzyfua++lHsgN+05EQAmIbJOnPT7ygqLh4pnQC1UTZczHbcXTRa3M1K511eqi5Tk3j9cy5fdW5WQIlD0C1OI6j7Izt+uaZx7R780bTcRACWvfqq7PuesB0DMC1KHsu5TiOnvlxvZ6axDo91IzPY+na4e01dkRHjlMAUGOO48hxHM399EPN/eR9BfyM8qFmhp5/qQaedha/n4BqoOy5kD9ga+n2PJ390izW6aFGuqQm6L/n9VGnpozmAahdjuMoJzNDXz39iPZs2WQ6DlzM4/XqvH89qqbtOrB+D6giyp7LOI6jUr+tE56apq3ZRabjwKUsS7pmWDvdPqqLPJYoegDqxIEdO6e/O04Lv/5M4iUHqimpaTNd+vjz8kZw1itQFZQ9F7r7sxV6e84W0zHgUo3jI/XMeX00uH1jfmECqDfbVi3XN888roIc1pmjenqfcLKOv+IvpmMArsLWRi7iD9ias3Gv3plL0UP1DO3QWD/eOpyiB6DetejaQ5c99aI6HHGk6ShwqaU/fKutK5ay2ytQBYzsuYTjOCoptzXyqananlNsOg5cxuexdOvITrp2eHtZYtomADMcx5FlWVry3dea+vbr8peXmY4El0lo1Fh/fvJFRURFybIYswAOh58Sl7AsS//6ciVFD1XWPClGn/xlsK4b3l4ei902AZhz4Pmn9wkn6+L//FfJzVsaTgS32bc3Sz++/iJFD6gkflJcwB+wNX39Hr0/f5vpKHCZE3uk6oexw9SzeRIlD0DQsCxLDdNa6OJHn1HXocNNx4HLrJo2WekL58oOBExHAYIe0ziDnO04Ki4LaMQTU7Uzv8R0HLhElM+jf/6pmy4+snXFtCkACDYHnp8Wf/eVprz5GmuxUGmxDZJ02VMvKSomVpaHsQvgj/DTEeQ8lqV7v1hJ0UOltUqO1dc3DdVFg1pJYn0egOB14Pmpzwmn6IJ/P6745EaGE8EtivJy9cMrz1L0gMPgJySI+QO2Jq/ZrY8XbjcdBS4xpEMjfXvz0WqfEk/JA+AalmWpSdv2uvTxF9Sye0/TceAS6+bM1JpZ05jOCRwC0ziDlG07Kizz67gnpmrPvlLTceAClw1po7tP6SaLQ9IBuJTjOJLjaNq747Tgy09Mx4ELRMcn6LKnXlJ0fII8jPIBv8FPRZDyeCzd9ekKih4OK8rn0eNn9dK9p3aXx8NumwDcy7IsWR6Pjrnoco2+7S5FxsSYjoQgV1KwT9+9+DRFD/gD/GQEIX/A1ncrduqLpRmmoyDINUmI0oTrjtKZ/VuYjgIAtarDgCN10cNPKzGliekoCHIbF83X6hlTmM4J/A7KXpCxHUeFpQH949PlpqMgyPVpmaTvbhmm7mmJjOYBCDmWZSkpNU0XP/KMUjt0Mh0HQe6nN19VeWmJWJ0EHIyyF2Q8lqV/fLpcewvLTEdBEDuzX3N9fO1gJcVGUPQAhCzLshQVF6fz/vWoOg4aYjoOglhxfp6mvv06vxOBX6HsBZEDh6d/vTzTdBQEKa/H0t1/6qonzukjL+vzAIQBy7Lk8Xo1+tY7NWD0mabjIIgt/+kHZaxbw3mNwC9Q9oKII+nuz1aYjoEgFRPh1f8uPUKXD2kriR03AYSPA893wy68TKOuvVker9dwIgQlx9H3Lz8jid+PwAGUvSBh245emJKuzXuLTEdBEGoYG6GPrh2sYZ1SKHkAwlr34cfr7HseUlRsnOkoCEJ7t2/VvM8/lmPbpqMAQYGyFwQCtqOd+SV64acNpqMgCLVoGKMvbxzKRiwAoP2jfM07d9NFD/9XiSlNTcdBEJr7yQfatzeL3TkBUfaCgtdj6Z+frVCpn3ehcLCuzRL01Q1D1TwphqIHAD+zLEsNmjbVxf95Rs06djYdB0HGX16m7195lum+gCh7xvkDtiat3qXJa3abjoIgM7hdI31y3VFqwI6bAPAblmUpKjZW5973H3U6kp06cbAtyxZrzcypjO4h7FH2DAs4ju77YqXpGAgyp/RsprevGKjoCC9FDwD+wIGdOk8de6f6/2mM6TgIMj+9+arKy0o5ew9hjbJnkO04eubHDdqeU2w6CoLIn49qo2cv6MvRCgBQCQeeJ4dffIUGnn624TQIJkV5uZy9h7BH2TMkYDvall2kV6dtNB0FQeTvJ3bWfaO7y2NR9ACgqo4+/1INOuNc0zEQRJZP/l6Z69cynRNhi7JniNdj6a5PV6gswKYskDyW9J8ze+q64R1MRwEAVxt67sUafNb5pmMgWDiOfvzfi2zWgrBF2TPAH7D1zfJMzdiQZToKgoDHkp44u7fOOaKl6SgAEBKOOvtCHXX2haZjIEjs2rhBK6f+yOgewhJlzwC/7ej+L1eZjoEg4PNYeua8vjq9b3OmbQJALRp81vk66tyLTcdAkJj+3psKBPymYwD1jrJXzxzH0RPfr9PO/BLTUWBYhNfS8xf01Sm9mlH0AKAODD7jXB19/qWmYyAIFOZka+4nH8ixWT6D8ELZq0cB29bGPYV6Y+Ym01FgWITX0ksX9tcJ3VMpegBQhwaefraGXXiZ6RgIAgu/+kwFOdmyKXwII5S9euT1eHTPFyvktznvJZwdKHrHdW1C0QOAejBg9JkafvEVpmPAMH95maa89Zo8Hl7+InzwaK8n/oCt6ev3aOaGvaajwCCfx9KLF/aj6AFAPev/pzEafulVpmPAsHVzZihj7Wo2a0HYoOzVE4/H0sPfrDEdAwb5PJZeuKCfRnRtStEDAAP6n3yajv3zNaZjwLAf33hJFqN7CBM80uuBP2Dr8yUZWpWZbzoKDPF6LD13fl+N7E7RAwCT+p10qo674jrTMWDQ7k3pHMWAsEHZqweOpCe+X2s6Bgx65IyeGtWDzVgAIBj0GXmyRlz5F4nn5LA14/23OIoBYYGyV8cCtqM3Z23W9pxi01FgyN9P7Kyzj2hJ0QOAIGFZlnoff5JGXnU9hS9MFeZka86E9zmKASGPslfHSsoDev6nDaZjwJArhrbVdcM7mI4BAPgVy7LU87hRGnbBn01HgSGLvv5chbk5FD6ENMpeHbJtR8//tEE5ReWmo8CAMX2b6+4/dTMdAwDwByzL0oDRZ6rX8SeZjgID/OVlmvnhO2zWgpDGo7uO2I6j7MIy/Y8D1MPSsZ2b6Imze8txOFMRAILd8Vdcp7Z9jjAdAwasnPqjcnft5KB1hCzKXh3xWJYe+36tSsp58gg3/Vo11MuX9JNliXV6AOAGlqXRt/1DKa3bmk6CeubYtma8/xYHrSNk8ciuAwHb0aasQn28cLvpKKhnHZvE6+0rBijC46HoAYBLWJYlb0SEzr77IcUnNzIdB/Vs7ezpytq2haMYEJIoe3XA67H00DerFbCZwhdOmifF6P2rj1RspI+iBwAuY1mWouPjdfbdDykyJsZ0HNQnx9H0d9+Ux+s1nQSodZS9WuYP2Fq8NUc/rNplOgrqUcPYCL139SAlx0VS9ADApSzLUsNmaTrtb/fwwj/MbFw0TzvT1zO6h5BD2atlPq9H//56tekYqEcRXkuvXnKEWjaMpegBgMtZlqWWXXto5NU3mI6Cejb93XGUfIQcyl4t8gdsTVq1Swu35JiOgnr0r9Hd1b91Q4oeAIQIy7LUY/hIDRh9pukoqEdbVyzVtpXLGd1DSKHs1SKf16NHv1tjOgbq0SWDW+uCQa0pegAQgoZdeJk6HTnUdAzUo+nvMbqH0ELZqyX+gK0fVu3Uul0FpqOgngzt0Fj3ndrddAwAQB065abblda5q+kYqCeZ69dq46L5CgT8pqMAtYKyV0t8Xo+e+XGD6RioJ20bx+mVi/uLAT0ACG2Wx6Mz7vyXkpo2Mx0F9WTG+2/J6/WZjgHUCspeLfAHbM1Yv0fLd+SZjoJ6kBjt01uXD1RMpJfpmwAQ4izLUmR0jM6+50HFJCSajoN6sGfLJq2dPZ21ewgJlL1a4PN69OxkRvXCgddj6YWL+qlFwxiKHgCECcuylNAoRaf//V55IyJMx0E9mPvph6zdQ0ig7NWQ37a1aEuO5m7KNh0F9eAfJ3fRkPaNKXoAEGYsy1KzDp006rpbTEdBPdizZZM2LVnA2j24HmWvhnwej575cb3pGKgH5xzRUlcMbUfRA4AwZVmWug45Rj1HjDIdBfVg7icfsnYPrkfZq4GAbWtVRr6mrNtjOgrqWK8WDfTwmB5yHMd0FACAQY7jaMTl16lxy9amo6CO7Vi7ShlrV7N2D65G2asBr8ejZyYzqhfqEqN9evWSI+TxWIzqAUCYsyxLHq9Xp/3tbvmiokzHQR2b/cn7rN2Dq1H2qilgO9qUVajvVu40HQV17PGze6tJQhRFDwAgaX/ha5DSVMdffp3pKKhjm5cs1J6tm2XbjO7BnSh71eT1WHp28noxqy+0XTK4tU7onkrRAwAcxLIsdR9+vLoMOcZ0FNSxuZ98II+H0T24E2WvGmzbUUZusb5YkmE6CupQj+aJuudP3UzHAAAEsVHX3aKk1DTTMVCH1s2Zqbzdu+TYtukoQJVR9qrBsqTnf9ogv82wXqhKiPLptUsGyOthRA8A8Me8Pp9O++s/5fWxa2Oochxbcz/9cP8LQMBlKHtV5DiO9haW6eOF201HQR169KxeaprIOj0AwKFZlqVGLVpq2EVXmI6COrRq2o8qys9jV264DmWvihxJL05JV6mfofxQddGRrXVSz2YUPQBApViWpX4nnar2RwwyHQV1JOD3a/4XE8RmDXAbyl4VFZb69d68raZjoI50T0vUfad24507AECVOI6jk2+8XQmNGpuOgjqy7IdvVVZSbDoGUCWUvSrw27bGz92qojK23w1FcZFevXLxEfJynh4AoIosy1JEVJRG33aXLA8vr0JReWmJlnz/DYesw1V4NqoCj2XpnTlbTMdAHfnnn7opLSmaogcAqBbLstS0XQcddfYFpqOgjiz9/hteJ8BVKHuV5A/Y+mnNbm3PYfg+FB3TKUXnD2zFEzgAoEYsy9KgMeeqVc/epqOgDuzbu0cbFsxVIOA3HQWoFMpeJfm8Ho2btdl0DNSBxGifnjq3D+v0AAC15tRb7lBsgyTTMVAHFn37hbxejtqAO1D2KsG2HW3ZW6gZG7JMR0EduOfUbmoYG8GoHgCgVliWpai4eJ184185my0EbV+1XHt3bOOQdbgCZa8yLOmNmZvZbTcEHd+1ic7q35KiBwCoVZZlqXXPPuo14kTTUVAHFn79GUUerkDZq4Qyv60JHKIecpJiI/T42b2ZvgkAqBOO42j4xVcoLqmh6SioZatnTFE5xzDABSh7h+EP2PpowXbtK2Uhbqi5/7QeahDD9E0AQN2wLEu+qCgd++erTUdBLfOXlmrZpIkcw4CgR9k7DJ/Xo7dmbzYdA7XsxB6pGt07jaIHAKhTlmWp8+Cj1aZ3P9NRUMuWfP81Zyoi6PEIPQS/bWvOxr1av7vAdBTUokZxkXr0zF5M3wQA1AvHcTTqulvki4wyHQW1KG/3Lm1cNJ/RPQQ1yt4h+DwctxCK/n16DyVE+xjVAwDUC8uyFJfUUEeeeZ7pKKhli7/9Qh6v13QM4A9R9v6A4zjanV+iH1btMh0Ftei4Lk10Us9mFD0AQL2yLEsDRp+pxi1bm46CWrRl+RLlZGYwWwhBi7L3B2xHenP2FgVsfnhDRZTPo4fP6MkTMgDACMuydNINt7Flf4hZ9O0XpiMAf4iy9wdsx9H787aajoFa9JdjO6hJQhSjegAAIyzLUpM27dRrxCjTUVCLVs/4SXaAXdsRnCh7v8MfsPXVsgztLSwzHQW1pE2jWF0/vD1FDwBg3PBLrlJsgyTTMVBLSgsLtX7ebAofghJl73f4vB69M4dRvVDy4Jge8nooegAA83yRkTru8mtNx0AtWvHTD/J4faZjAL9B2fsVx3G0PadIC7fkmI6CWnJij1QN6ZDCqB4AIChYlqXORw5Va87eCxlbly9VQU626RjAb1D2fsV2pI8WbDcdA7UkNtKrB0/rwaYsAICg4jiORl17M2fvhQjHsbV88vecuYegQ9n7Fa/H0ieLKXuh4qbjOio5PpJRPQBAULEsS/ENk3XkGeeajoJasnLqJM7cQ9Ch7P1CwHa0cEuOtmUXm46CWtChSbyuGtaOogcACEoHzt5rxNl7ISFv105tX72S0T0EFcreL3gs6eOF20zHQC15+IweYk8WAEAwszwenXDtTZy9FyKWT/6O0T0EFcreL/gDjr5enmk6BmrBiT1SNaBNI0b1AABBzbIspXXorG5HH2s6CmrBurkzVV5aajoGUIGy9zN/wNYPq3cpv5gzUtzO57F09yld2ZQFAOAKjuPo6AsuZev+EOAvLdWamVM4cw9Bg7L3M5/XowmL2JglFJw7oKXSkmIY1QMAuIJlWYpLSlbPESeYjoJasGLKJIo7ggZl72d5xeWaunaP6RioodhIr/52YhfTMQAAqLIh51zEUQwhIGPtauXuymSGEYICZU/7p3B+smi7/DY/lG53xdC2Soz2MaoHAHAVy7IUHZ+gPqNOMR0FtWD55O8pewgKlD3tn8L5yaIdpmOghpLjInXDsR0oegAAV7IsS4PPPF+RMTGmo6CG1sycJo+Hl9kwL+wfhY7jaHNWoZbvyDMdBTV043EdFOkL+4c0AMDFIqKj1f+UMaZjoIby9+zSzvT1cmzbdBSEubB/ZWw70ocLOFvP7Volx+qSI1szqgcAcDXLsjTwtLMUk5BoOgpqaPWMn0xHACh7Xo+lzxYzhdPtbh/VWR5OUAcAhABvRIQGjD7TdAzU0LrZMyXehIZhYV32AratORv3KiOvxHQU1EDP5g10au80RvUAACHBsiz1P/k0xTVMNh0FNVCQs1cZa1fLZionDArrsuexLH3KqJ7r/ePkLux4BQAIKZbXq8Fnnmc6Bmpo9YyfeDMaRoV12XMkTVq1y3QM1EC/Vkka3L4xT6QAgJBiWZZ6jjhRiSlNTUdBDaybO0viDWkYFLZlz7YdLd6aq72FZaajoAZuHtGRUT0AQEiyLEtDzr3IdAzUQHF+nnasXcVUThgTtmXPkfTtikzTMVAD3Zol6pjOTRjVAwCEJMuy1HXIMUpu3sJ0FNTA2lnTxSsVmBK2Zc/rsfQDUzhd7Ybj2jOqBwAIbZalIeddYjoFamD9vFnsygljwrLsOY6j9D0F2rK3yHQUVFO7xnE6sUczRvUAACHNsix1GniUmrRtbzoKqqkwN0cZ69iVE2aEZdkL2I6+Wc4UTje7bnh7pkQAAMKC4zg6mtE9V2MqJ0wJy7Ln83r0/UqmcLpV86QYndGvBaN6AICwYFmW2vTpr9QOnUxHQTWtnzdLlicsX3bDsLB81O3ZV6rlO/JMx0A1XT2snTz0PABAGHEcR/1PPs10DFRTQfZe7dm6mb0GUO/CruyVB2ymcLpYSnyULhzUilE9AEBYsSxLnY4cqviGjUxHQTWlL5gjh3V7qGdhV/YivB524XSxK45uKw/DegCAMGR5POp9wkmmY6CaNi5aII/XazoGwkzYlb3CUr/mbtprOgaqITHapz8f1UYeRvUAAGHIsiz1PXG0vBERpqOgGnZuWKeSggLTMRBmwqrslQdsTVq9S+UB5ku70Zn9WyjKF1YPWQAADhIZE6OuQ4abjoFqcBxbGxfPVyDgNx0FYSSsXjlHeD36buVO0zFQDZYlXXV0O9MxAAAwbsDpZ5mOgGrauGi+vF6f6RgII2FV9soDtqau3WM6Bqrh6A6NlZYUw8YsAICwZlmWkps1V4tuPU1HQTVsXrqQw9VRr8Km7AVsW7M2ZKmwLGA6Cqrh8qFt2a4YAADtP4bhiFNONx0D1VBaWKjM9WvYlRP1JmzKnmVZ+p5dOF2pZXKMhnVKYVQPAADtf03Trv9AJaY0NR0F1ZC+YK4c8QY26kfYlD2PZWnaeqZwutFFg1qLmgcAwMH6nvgn0xFQDRsXL5DHwxEMqB9hU/Yy84q1LbvYdAxUUXSERxcd2ZpRPQAAfsGyLPU+/iRFREWbjoIq2rttiwqyOQYM9SMsyl55wNa0dVmmY6AaRvdurthI3v0CAODXfFFR6n7MCNMxUA0bFsxRwM8RDKh7YVH2IrwezUqn7LnRlUe3MR0BAICgdcSpY0xHQDVsXDRfXh9HMKDuhUXZk6TZ6QyXu03/1g3VqWkiUzgBAPgdlmWpQZNUNe/S3XQUVNH2VSs4ggH1IizK3qasQu3eV2o6BqroksGtOW4BAIBDcBxHPYYfbzoGqqi8tES7N27gdQ7qXMiXvf3r9diF020Sonw6uWczRvUAADgEy7LUZegxbNTiQltWLJUd4Pxn1K2QL3v71+sxhdNtTuyZKp+HogcAwOF4fRHqNHio6Rioom0rl7FuD3Uu5Mue7Tias5Gy5zbnDWhlOgIAAK7R6/gTTUdAFWWsXc3IHupcSJc9x3G0duc+5RWXm46CKmieFKP+rRsyhRMAgEqwLEtpHbsoKTXNdBRUQXlpiXZtYt0e6lZIlz2/7bBez4VO79ucJz4AAKrAcRzO3HOhrctZt4e6FdJlL8Lr4cgFFzp/YEvTEQAAcBXLstRrxChZVki/tAs5rNtDXQvpZwS/bWve5mzTMVAFvVs0UIuGsUzhBACgimIbJKlVrz6mY6AKdqxj3R7qVsiWPcdxtGx7norK+AFykzH9WjCFEwCAanAcR12GHGM6BqrAX1qqXZy3hzoUsmUvYDuavj7LdAxUQYTX0pn9mjOqBwBANViWpc5HDpE3IsJ0FFTBluVL5NgMTqBuhGzZ83k9mp1O2XOT4Z2bKCGaX1AAAFRXRFS02vbubzoGqmDbqmXyeFm3h7oRsmXPH7C1eGuu6RiogjP7sQsnAAA14TiOOg8ZZjoGqiBj3RrW7aHOhGzZW7erQKV+23QMVFJ0hEfHdW3CFE4AAGrAsix1HDBYvsgo01FQSf7SUu3evNF0DISokCx75QFbi7bmmI6BKji6Y4oivV7TMQAAcD1vRITa9OlnOgaqIGPdagX8ftMxEIJCsuz5PJaWbs81HQNVcEK3pkzhBACgFjiOo85HDjUdA1WwM3095+2hToTko8qyLC3blmc6BirJ67F0cs9mTOEEAKAWWJalDgMGyxcRKX95mek4qISdG9aZjlDrHMeR3+9XgPWItS4iIkLeSs6IC8myV1Ie0IY9BaZjoJL6t26ouKiQfCgCAGCELzJSrXv3VfqCuaajoBJydmaorLhYkTExpqPUirKyMmVmZqqoqMh0lJBkWZZatGih+Pj4w1435F5hO46jlTvyFLCZEugWB6ZwMrIHAEDtcBxH7fsPouy5heNo58b1atmtp+tfD9m2rU2bNsnr9SotLU2RkZGuv0/BxHEc7dmzR9u3b1fHjh0PO8IXcmXPbztatC3XdAxUwam903gSAACgFh2Yyvn9K89KrIl3hcx1a9S8czfXr90rKyuTbdtq2bKlYmNjTccJSSkpKdq8ebPKy8sPW/ZCboOWCK9Hyyh7rtG5aYKaJkabjgEAQMiJSUhQkzbtTMdAJe3csM71Re+XPJ6QqxlBoyqDJCH5r7B0O5uzuMUJ3dmFEwCAuuA4jtr1G2A6BippZ/p60xEQgkKu7OWXlGtrNotB3eJPvZqZjgAAQMjqOPAo0xFQSQU5e1WUl2s6BmpoypQpsixLubm5pqNICrGyZzuOljKF0zXSGkSrc2oi6/UAAKgDlmUppXVbxSQ2MB0FlZSxbrVsO3yOKti5c6duvPFGtWvXTlFRUWrZsqVOPfVU/fjjj5X6/HHjxikpKaluQ1bRUUcdpczMTDVoEBw/d6FV9mxHSyh7rnFclyZM4QQAoI617dPfdARUUuaGdVKYvDTavHmz+vfvr8mTJ+uxxx7T8uXLNXHiRB177LG6/vrrTcerlvLyckVGRio1NTVoBjNCquz5vB5G9lxkSMfGpiMAABDy2h0xyHQEVNLO9HXyVPKwbLf7y1/+IsuyNG/ePJ155pnq1KmTunfvrltvvVVz5syRJD355JPq2bOn4uLi1LJlS/3lL39RQcH+s7SnTJmiyy67THl5ebIsS5Zl6b777pMklZaW6q9//auaN2+uuLg4DRo0SFOmTDno67/66qsVO4aOGTNGTz755G9GCV988UW1b99ekZGR6ty5s95+++2DLrcsSy+++KJGjx6tuLg4Pfjgg787jXPGjBk6+uijFRMTo5YtW+qmm25SYWFhxeUvvPCCOnbsqOjoaDVt2lRnnXVW7XyTFWJlT2JzFrewLGlYx5SgedcDAIBQZFmW2vU5ImwKhNvtSt9gOkK9yM7O1sSJE3X99dcrLi7uN5cfKF0ej0fPPPOMVq5cqTfffFOTJ0/W3/72N0n7p0s+/fTTSkxMVGZmpjIzM/XXv/5VknTDDTdo9uzZev/997Vs2TKdffbZOvHEE7V+/f5NcGbOnKlrr71WN998s5YsWaKRI0fqwQcfPCjDp59+qptvvlm33XabVqxYoWuuuUaXXXaZfvrpp4Oud99992nMmDFavny5Lr/88t/cl/T0dJ144ok688wztWzZMn3wwQeaMWOGbrjhBknSggULdNNNN+n+++/X2rVrNXHiRA0bNqxm3+BfsJwQmke3Z1+pBjw4yXQMVEK3Zon65uajTccAACAsvH/P37Rj7SrTMVAJ1778tuKSGpqOUW0lJSXatGmT2rZtq+jo3z9ea968eRo0aJA++eQTjRkzptK3/fHHH+vaa69VVlaWpP1r9m655ZaDRtG2bt2qdu3aaevWrUpLS6v4+PHHH6+BAwfqoYce0nnnnaeCggJ99dVXFZdfdNFF+uqrrypua8iQIerevbteeeWViuucc845Kiws1Ndffy1p/5spt9xyi5566qmK60yZMkXHHnuscnJylJSUpCuvvFJer1cvv/xyxXVmzJihY445RoWFhfrmm2902WWXafv27UpISKjU96Ey3+MDQmZkL2A7Wrw1x3QMVNLg9o1YrwcAQD1wHEfNu3Y3HQOVtGfLxpB/jVTZ+zdp0iSNGDFCzZs3V0JCgi6++GLt3btXRUV/vPP+8uXLFQgE1KlTJ8XHx1f8mTp1qtLT0yVJa9eu1cCBAw/6vF//ffXq1RoyZMhBHxsyZIhWr1590MeOOOKIQ96HpUuXaty4cQdlGTVqlGzb1qZNmzRy5Ei1bt1a7dq108UXX6zx48cf8v5VVcic3Gg7jlZn5puOgUoaxno9AADqTasevTXvs49Mx0AlZG3dopbde4fUAeu/1rFjR1mWpTVr1vzhdTZv3qw//elPuu666/Tggw8qOTlZM2bM0BVXXKGysjLFxsb+7ucVFBTI6/Vq4cKF8v5q+nJ8fHyt3g9JvzsN9dd5rrnmGt10002/uaxVq1aKjIzUokWLNGXKFH3//fe65557dN9992n+/Pm1stNoyIzsRXg92rC7wHQMVILPY+nIdo1YrwcAQD2wLEvNu3ST5QmZl30hLWv71pBfY5mcnKxRo0bp+eefP2ijkgNyc3O1cOFC2batJ554QkceeaQ6deqkjIyMg64XGRmpQODgoyr69u2rQCCg3bt3q0OHDgf9SU1NlSR17txZ8+fPP+jzfv33rl27aubMmQd9bObMmerWrVuV7mu/fv20atWq32Tp0KGDIiMjJUk+n0/HH3+8Hn30US1btkybN2/W5MmTq/R1/khI/dRv2EPZc4NeLRooKiK0n8QAAAgmvohINW3bwXQMVELWti1h8Yb4888/r0AgoIEDB2rChAlav369Vq9erWeeeUaDBw9Whw4dVF5ermeffVYbN27U22+/rZdeeumg22jTpo0KCgr0448/KisrS0VFRerUqZMuvPBCXXLJJfrkk0+0adMmzZs3Tw8//HDFWrsbb7xR33zzjZ588kmtX79eL7/8sr799tuDvu+33367xo0bpxdffFHr16/Xk08+qU8++aRiE5jK+vvf/65Zs2bphhtu0JIlS7R+/Xp9/vnnFRu0fPXVV3rmmWe0ZMkSbdmyRW+99ZZs21bnzp1r+B3eL2TKnu042pT123cGEHwGt28c8nPRAQAIJo7jqAXr9lwhe/s20xHqRbt27bRo0SIde+yxuu2229SjRw+NHDlSP/74o1588UX17t1bTz75pP7zn/+oR48eGj9+vB5++OGDbuOoo47Stddeq3PPPVcpKSl69NFHJUlvvPGGLrnkEt12223q3LmzTj/9dM2fP1+tWrWStH/t3UsvvaQnn3xSvXv31sSJEzV27NiDNjs5/fTT9d///lePP/64unfvrpdffllvvPGGhg8fXqX72atXL02dOlXr1q3T0Ucfrb59++qee+6p2DwmKSlJn3zyiY477jh17dpVL730kt577z117147P68hsxtnZl6xBj9cO8OdqFsfXHOkBrZJDot3rQAACAaO42jzkoX65JH7TEdBJVz9wjglNHLn/gZV2SkymFx11VVas2aNpk+fbjrKYVXlexwSKz8dx9H6XUzhdIMon0f9WjWk6AEAUI8sy1KLbj1lWR45jm06Dg5jz5aNim+YzDrLOvT4449r5MiRiouL07fffqs333xTL7zwgulYtS4kHkHlAUfrd+8zHQOV0K91Q0V4Q+JhBwCAq0RERalxq9amY6ASsrZukW1TyuvSvHnzNHLkSPXs2VMvvfSSnnnmGV155ZWmY9W6kBjZ83ktpe9mvZ4b9G/dUI7jMLIHAEA9279ur4f2bNlkOgoOI2vblpA+eiEYfPjhh6Yj1IuQGGLxWBY7cbpEv5ZJpiMAABC2WnbvZToCKmHv9q2mIyBEhETZk6R0zthzhSPYmAUAACMsy1KrHpQ9N8jesV0O0zhRC0Ki7O0rKdfewjLTMXAYqYnRSoyJMB0DAICwFRUbp+TmLUzHwGH4y8uUn7XHdAyEgJAoe+l7WK/nBr1bNjAdAQCAsLZ/3V5P0zFQCXu3b+VcYtSY68teecDWul3sxOkGvVok8aQFAIBhaZ26mI6ASsjbvVN2wG86BlzO9WXPY1ms13OJfq2STEcAACCsWZallDbtTMdAJeTt3sU5e6gx1z+CvB524nSL3i2T2JwFAADDkpu3kPh9HPTydu2Ux+M1HQMu5/qyJ7ETpxu0aRSr2EjOiwEAwDSfL0INmjQ1HQOHkbdnl+kItS5g199ynqp+rUAgoLvvvltt27ZVTEyM2rdvrwceeOAPlyBde+21sixLTz/99EEfb9OmjSzLOujPI488Ut27UWOuf/XtD9jallNsOgYOo1eLJNMRAADAzxq3aKW8XTtNx8Ah5O0OvX8fr8fSze8v1oY6Hqjp0CRe/z2vb5U+5z//+Y9efPFFvfnmm+revbsWLFigyy67TA0aNNBNN9100HU//fRTzZkzR2lpab97W/fff7+uuuqqir8nJCRU/U7UEteXvV35pfX6LgGqp3fLBnIch2mcAAAY5jiOGrVsrfSF80xHwSGUFRertKhQUbFxpqPUqg27C7QyI990jN+YNWuWTjvtNJ1yyimS9o/Qvffee5o37+Cfkx07dujGG2/Ud999V3HdX0tISFBqamqdZ64M10/j3JHLqJ4b9Gvd0HQEAADws5TWbU1HQCXk7Q69qZzB6qijjtKPP/6odevWSZKWLl2qGTNm6KSTTqq4jm3buvjii3X77bere/fuf3hbjzzyiBo1aqS+ffvqsccek99vbldVV4/s+W1bGZQ9V+jcNIFRPQAAgoBlWWrWoZPpGKiEnMwdatyqjTzsylnn7rjjDuXn56tLly7yer0KBAJ68MEHdeGFF1Zc5z//+Y98Pt9vpnX+0k033aR+/fopOTlZs2bN0p133qnMzEw9+eST9XE3fsPVZc+2pZ15JaZj4DAax0eyOQsAAEEkoVGKLI9Hjm2bjoJDyNu9a/+/EWWvzn344YcaP3683n33XXXv3l1LlizRLbfcorS0NF166aVauHCh/vvf/2rRokWHHMC49dZbK/6/V69eioyM1DXXXKOHH35YUVFR9XFXDuLqR47PY2lnPmUv2HVoEm86AgAA+AWP16ukps1Mx8Bh5O3eJY+X4xfqw+2336477rhD5513nnr27KmLL75YY8eO1cMPPyxJmj59unbv3q1WrVrJ5/PJ5/Npy5Ytuu2229SmTZs/vN1BgwbJ7/dr8+bN9XNHfsXVwy0ej6VMRvaCXvuUeDZnAQAgyDRu2Vo5mTtMx8Ah5O3ZxeunelJUVPSb6bJer1f2z6PfF198sY4//viDLh81apQuvvhiXXbZZX94u0uWLJHH41GTJk1qP3QluLrsSdIuRvaCXvsURvYAAAgm+3fkbKX182aZjoJD4HiM+nPqqafqwQcfVKtWrdS9e3ctXrxYTz75pC6//HJJUqNGjdSoUaODPiciIkKpqanq3LmzJGn27NmaO3eujj32WCUkJGj27NkaO3asLrroIjVsaGazQteXvcw8NmgJdp1SKXsAAASblFZtTEfAYezL2m06Qq2rj+U91fkazz77rO6++2795S9/0e7du5WWlqZrrrlG99xzT6VvIyoqSu+//77uu+8+lZaWqm3btho7duxB6/jqm6vLnm07yiooMx0Dh9E1NZEpCAAABBHLstS0fUfTMXAYAb9fpYWFiooLjbP2ArZT5cPOa/K1vJ7Kv/5MSEjQ008/raeffrrSn/PrdXj9+vXTnDlzKv359cHVG7RkF5ZxoHqQi4nwqlF8/e88BAAADi0hubE8Xle/7x8WivLzTEeoNVUpX276WsHM1WWPnTiDX7uU0HgnCgCAUOPxetUwrbnpGDiMgpy9piPAxVxb9mzb0bacItMxcBhszgIAQPBq3KKV6Qg4jMKcbNmBgOkYcCnXlr2A7XCgugu0bxIv22GqLQAAwcZxHDWi7AW9wrxcObyWQjW5tuxZHlH2XKB9SpyYMQ0AQHBqzI6cQa8oL1fsc4fqcm3Z83k8rNlzgbaN49iJEwCAIGRZlhq3bG06Bg6jKDeXjXRQba4texIje26Q1iDGdAQAAPAHYhs0MB0Bh1GYl2M6AlzM3WWPkb2g5rGkxJgI0zEAAMAfiIyJleVx9cvBkFeUl2s6AlzM1T/dezlQPag1jo/ijBMAAIKYZVmKSUg0HQOHQNlDTbi27NmOo8Iyv+kYOITUBtGmIwAAgMOIS2poOgIOgbKHmnBt2SsuC4hdaINbaiJlDwCAYBdL2QtqAb9fpUUhcra0XY/nBVbxaz388MMaMGCAEhIS1KRJE51++ulau3btQdcZPny4LMs66M+1115bcfnevXt14oknKi0tTVFRUWrZsqVuuOEG5efn18pdqg7Xbu1TUMqoXrBLbRAtx3HYjRMAgCAW1yDJdAQcRnF+nqJiY03HqDmPV5pwpZS1rm6/TuNO0pmvVelTpk6dquuvv14DBgyQ3+/XP/7xD51wwglatWqV4uLiKq531VVX6f7776/4e+wv/l08Ho9OO+00/fvf/1ZKSoo2bNig66+/XtnZ2Xr33Xdrfr+qwbVlb18JZS/YpSZGy5E4Zw8AgCDlOI5iKXtBr2hfnpJSm5mOUTuy1kmZS02n+I2JEyce9Pdx48apSZMmWrhwoYYNG1bx8djYWKWmpv7ubTRs2FDXXXddxd9bt26tv/zlL3rsscfqJnQluHYaZ35xuekIOIzUBtEUPQAAghwje8GvtLDAdISwk5eXJ0lKTk4+6OPjx49X48aN1aNHD915550qOsQU24yMDH3yySc65phj6jTrobh2ZC+Pshf0mifFMIUTAIAgZlkWa/ZcoKyoSLZty8MxGfXCtm3dcsstGjJkiHr06FHx8QsuuECtW7dWWlqali1bpr///e9au3atPvnkk4M+//zzz9fnn3+u4uJinXrqqXrttapNKa1Nrix7AdtWfgllL9i1TA6BueUAAIS4xJQmpiPgMMpKiuXYtkTZqxfXX3+9VqxYoRkzZhz08auvvrri/3v27KlmzZppxIgRSk9PV/v27Ssue+qpp3Tvvfdq3bp1uvPOO3XrrbfqhRdeqLf8v+TKsmc7rNlzg0bxkaYjAACAw0hIbmQ6Ag6jrLhYEtvQ14cbbrhBX331laZNm6YWLVoc8rqDBg2SJG3YsOGgspeamqrU1FR16dJFycnJOvroo3X33XerWbP6X3fpyrLnONI+RvaCWoTXUpTPazoGAAA4jGgOVQ96+8seS2PqkuM4uvHGG/Xpp59qypQpatu27WE/Z8mSJZJ0yBJn27YkqbS0tFZyVpUry55lMbIX7BKiI0xHAAAAlRAZFS3L8shxbNNR8AfKSorZB6GOXX/99Xr33Xf1+eefKyEhQTt37pQkNWjQQDExMUpPT9e7776rk08+WY0aNdKyZcs0duxYDRs2TL169ZIkffPNN9q1a5cGDBig+Ph4rVy5UrfffruGDBmiNm3aGLlfrix7Xo9F2QtyCdGufGgBABB2LI9H0QkJKs7PMx0Ff6CsuFhWqKzXa9wpKL/Giy++KGn/wem/9MYbb+jPf/6zIiMjNWnSJD399NMqLCxUy5YtdeaZZ+qf//xnxXVjYmL06quvauzYsSotLVXLli11xhln6I477qjR3akJV74i91iUvWAXH+XKhxYAAGEpLqkhZS+IhczInh2o8mHnNfpansovKXKcQ6+JbNmypaZOnXrI6xx77LGaNWtWpb9mfXDtWwT7SlmzF8wSmcYJAIBrcNZecCs7xFlurlKF8uWqrxXEXFv2ChjZC2pM4wQAwD1iKXtBrayk2HQEuJRryx7TOIMbZQ8AAHdwHIeD1YNcWXGIjOyh3rm27HGoenBLiI447NxnAAAQBBxHMfEJplPgEMoZ2UM1ubbsFZUFTEfAIcRH+zj6EwAAN7Akj5f1TcFs/zl7QNW5tuyV+zkLJpglsBsnAAAuYYXOtv4hKuBn+RKqx7U/2X6bcaNglhAdoRDYIBgAgLBA2QtudoAZbage1/5k+21G9oJZIhu0AADgGh7KXlBzeN2LanLtTzYje8Et0ld7D61HHnlElmXplltuqfhYenq6xowZo5SUFCUmJuqcc87Rrl27Dnk7bdq0kWVZv/lz/fXXV1zn1ltvVXJyslq2bKnx48cf9PkfffSRTj311Fq7XwAABAtG9oIbZQ/V5cqfbNtxxEaPwS3S65Fl1Xwi5/z58/Xyyy+rV69eFR8rLCzUCSecIMuyNHnyZM2cOVNlZWU69dRTZR/iyXD+/PnKzMys+PPDDz9Iks4++2xJ0pdffql3331X33//vR599FFdeeWVysrKkiTl5eXprrvu0vPPP1/j+wQAQDCxLEseLzNygtmhXt8Ah+LOsseoXtCL8NW86BUUFOjCCy/Uq6++qoYN///8n5kzZ2rz5s0aN26cevbsqZ49e+rNN9/UggULNHny5D+8vZSUFKWmplb8+eqrr9S+fXsdc8wxkqTVq1dr+PDhOuKII3T++ecrMTFRmzZtkiT97W9/03XXXadWrVrV+H4BABBsvD7KXjCz7dBYsxeox/tR06/1ezPLrrnmGrVv314xMTFKSUnRaaedpjVr1vzu5+/du1ctWrSQZVnKzc096LIpU6aoX79+ioqKUocOHTRu3LgaZT0UV/5kM4Uz+EV4ar6F8/XXX69TTjlFxx9/vP79739XfLy0tFSWZSkqKqriY9HR0fJ4PJoxY4aOP/74w952WVmZ3nnnHd16660VI5C9e/fWK6+8opycHG3cuFHFxcXq0KGDZsyYoUWLFumFF16o8X0CACAYMY0zyIXIlDavx6s7pt2hjXkb6/TrtGvQTo8Me6Tan/97M8skqX///rrwwgvVqlUrZWdn67777tMJJ5ygTZs2yfur40uuuOIK9erVSzt27Djo45s2bdIpp5yia6+9VuPHj9ePP/6oK6+8Us2aNdOoUaOqnfmPuLLsMbIX/Go6svf+++9r0aJFmj9//m8uO/LIIxUXF6e///3veuihh+Q4ju644w4FAgFlZmZW6vY/++wz5ebm6s9//nPFx0aNGqWLLrpIAwYMUExMjN58803FxcXpuuuu07hx4/Tiiy/q2WefVePGjfXKK6+oe/fuNbqPAAAEC08tvEmLumXbdkhspLMxb6NWZ682HeMP/XJm2S8HGyTp6quvrvj/Nm3a6N///rd69+6tzZs3q3379hWXvfjii8rNzdU999yjb7/99qDbeOmll9S2bVs98cQTkqSuXbtqxowZeuqpp+qk7LnyEcOs5eAX4a3+Q2vbtm26+eabNX78eEVHR//m8pSUFH300Uf68ssvFR8frwYNGig3N1f9+vWr9JPg66+/rpNOOklpaWkHffy+++7Thg0btHz5co0ZM0YPP/ywjj/+eEVEROjf//63ZsyYoSuvvFKXXHJJte8fAADBxqrB723UDzZpqR+/nFl2KIWFhXrjjTfUtm1btWzZsuLjq1at0v3336+33nrrd1+Xzp49+ze3PWrUKM2ePbt27sCvuHJkD8GvJnuzLFy4ULt371a/fv0qPhYIBDRt2jQ999xzKi0t1QknnKD09HRlZWXJ5/MpKSlJqampateu3WFvf8uWLZo0aZI++eSTQ15vzZo1euedd7R48WL973//07Bhw5SSkqJzzjlHl19+ufbt26eEhITq31EAAIKENwRGjEKd41D26tqhZpYd8MILL+hvf/ubCgsL1blzZ/3www+KjIyUtH+p0fnnn6/HHntMrVq10saNv52uunPnTjVt2vSgjzVt2lT5+fkqLi5WTExMrd4nfrIRdEaMGKHly5dryZIlFX+OOOIIXXjhhVqyZMlBc6IbN26spKQkTZ48Wbt379bo0aMPe/tvvPGGmjRpolNOOeUPr+M4jq655ho9+eSTio+PVyAQUHl5uSRV/DfAAacAgBBhsRtn0GNkr24dbmbZARdeeKEWL16sqVOnqlOnTjrnnHNUUlIiSbrzzjvVtWtXXXTRRfUV+7Aoewg6CQkJ6tGjx0F/4uLi1KhRI/Xo0UPS/sI2Z84cpaen65133tHZZ5+tsWPHqnPnzhW3M2LECD333HMH3bZt23rjjTd06aWXyneIncdee+01paSkVJyrN2TIEE2ePFlz5szRU089pW7duikpKan27zwAAAZ4PDXfRRt1i7JXt345s8zn88nn82nq1Kl65pln5PP5Kt7kb9CggTp27Khhw4bp448/1po1a/Tpp59KkiZPnqyPPvqo4vNHjBghaf/gxL333itJSk1N/c3Z0Lt27VJiYmKtj+pJTOOES61du1Z33nmnsrOz1aZNG911110aO3bsQdc5MM3zlyZNmqStW7fq8ssv/8Pb3rVrlx588EHNmjWr4mMDBw7UbbfdplNOOUVNmjTRm2++Wbt3CAAAkyw2aAl2DhsU1qkDM8t+6bLLLlOXLl3097///Te7bUr7Z4I5jqPS0lJJ0oQJE1RcXFxx+fz583X55Zdr+vTpFRu4DB48WN98881Bt/PDDz9o8ODBtX2XJFH2UFdq+floypQpB/39kUce0SOPHHpL3c2bN//mYyeccIKcw2xf3LRp09/93HvuuUf33HPP4aICAOA6jOwFP9bs1a0DM8t+6ZczyzZu3KgPPvhAJ5xwglJSUrR9+3Y98sgjiomJ0cknnyxJB+3IKali0KFr164VM8KuvfZaPffcc/rb3/6myy+/XJMnT9aHH36or7/+uk7uF2UPdYL3ngAAcA+LoxeCnidEDr5v1+Dwm+kF49eIjo7W9OnT9fTTTysnJ0dNmzbVsGHDNGvWLDVp0qTSt9O2bVt9/fXXGjt2rP773/+qRYsWeu211+rk2AWJsoc64g/w7hMAAG7h4eiFoOcNgU10AnagRoedV/VreWv4JsYvZ5alpaX9Zvrl4QwfPvx3Z5QNHz5cixcvrlG2ynLlTzYTDYJfYZnfdAQAAFBJjOwFv1AY2atp+QrWrxXMXFn2anJgN+pHQQnHEgAA4BqHWc8OsyyPR1ZNDjFG2HJla4r0eWp0aDfqXmGZ/7AboQAAAPMcx1FJwT7TMXAI3ogI0xHgUq4se5IUE8HQbDArKWdkDwAAtygpKjQdAYfg81H2UD2UPdSJojLKHgAAblFaUGA6Ag4hFNbrwQz3lr1Iyl4wK2ZkDwAA1ygppOwFM6ZxorrcW/YY2QtqxWUBztoDAMAlSosoe8HMR9lDNbm27MUyshfUissDHJEBAIALWJalEqZxBjUva/ZQTa4te0zjDG5FZQG2CAYAwCVKmcYZ1Fizh+pyb9mL4EEfzIrZoAUAANdgN87gxjROVJd7y16ka6OHBTZoAQDAPdiNM7iFygYtTqD+Xh9W52tNmzZNp556qtLS0mRZlj777LPfXGf16tUaPXq0GjRooLi4OA0YMEBbt26VJGVnZ+vGG29U586dFRMTo1atWummm25SXl7eb25n3Lhx6tWrl6Kjo9WkSRNdf/31Vc5bGa4dHouNdG30sFBQ4jcdAQAAVBK7cQa3iKho0xFqheX1asdfb1fZxo11+nUi27VT88cfq/LnFRYWqnfv3rr88st1xhln/Oby9PR0DR06VFdccYX+9a9/KTExUStXrlR09P5/n4yMDGVkZOjxxx9Xt27dtGXLFl177bXKyMjQxx9/XHE7Tz75pJ544gk99thjGjRokAoLC7V58+Zq399DcWVjsh1H0ezGGdT2FJSajgAAACqJshfcouLiTUeoNWUbN6pk1SrTMX7XSSedpJNOOukPL7/rrrt08skn69FHH634WPv27Sv+v0ePHpowYcJBlz344IO66KKL5Pf75fP5lJOTo3/+85/68ssvNWLEiIrr9urVq5bvzX6unAtp2w67cQa5LMoeAACuYNu2/KX83g5m0XFxsm3bdIywZtu2vv76a3Xq1EmjRo1SkyZNNGjQoN+d6vlLeXl5SkxMlO/nTXZ++OEH2batHTt2qGvXrmrRooXOOeccbdu2rU5yu7LsOeKcvWCXV1yuAE9KAAAEPYpe8IuKi5fD6yqjdu/erYKCAj3yyCM68cQT9f3332vMmDE644wzNHXq1N/9nKysLD3wwAO6+uqrKz62ceNG2bathx56SE8//bQ+/vhjZWdna+TIkSorK6v13K6cxuk4HL0Q7Bxn/7q9BrGRpqMAAIBDKCsuMh0BhxEdF6/9wx0w5cDI6mmnnaaxY8dKkvr06aNZs2bppZde0jHHHHPQ9fPz83XKKaeoW7duuu+++w66nfLycj3zzDM64YQTJEnvvfeeUlNT9dNPP2nUqFG1mtuVI3uyGNlzA9btAQAQ/Ir37TMdAYcRFRcvWe582R4qGjduLJ/Pp27duh308a5du1bsxnnAvn37dOKJJyohIUGffvqpIn6xm2qzZs0k6aDbSUlJUePGjX9zO7XBlY8aS2LNngtk5BSbjgAAAA6jMC/HdAQcRnRcvDweV75sDxmRkZEaMGCA1q5de9DH161bp9atW1f8PT8/XyeccIIiIyP1xRdfVOzUecCQIUMk6aDbyc7OVlZW1kG3U1tcOY3T67GUGBMa542Est0FpXIcR5ZlmY4CAAB+h+M4KtmXbzoGDiMmIZHXU/WgoKBAGzZsqPj7pk2btGTJEiUnJ6tVq1a6/fbbde6552rYsGE69thjNXHiRH355ZeaMmWKpP8vekVFRXrnnXeUn5+v/Pz9P18pKSnyer3q1KmTTjvtNN1888165ZVXlJiYqDvvvFNdunTRscceW+v3yZVlz2NZSk0MjfNGQtmefUzjBAAg2JUUFpqOgMOITkgwHaHWRLZrF7RfY8GCBQcVrltvvVWSdOmll2rcuHEaM2aMXnrpJT388MO66aab1LlzZ02YMEFDhw6VJC1atEhz586VJHXo0OGg2960aZPatGkjSXrrrbc0duxYnXLKKfJ4PDrmmGM0ceLEg6Z71hZXlj1JapIQZToCDiOroPZ3FAIAALXIcVRSwJq9YBcdIufsOYFAtQ47r+7XsrxVW/Y1fPhwOc6hN8K5/PLLdfnll1f78yUpMTFRr7/+ul5//fUq5asO107+bRjHLo/Bbs++UqYcAAAQzCxL+Xt2m06Bw4iMiTUdoVZUtXy55WsFM9eWvegIL5u0BDkOVgcAILhZlqWcnRmmY+AQPF6vIqKY0YbqcW3Zk6TG8Tzwg9mufMoeAADBLjeTshfMomLjTEeAi1H2UGe25xTJrsS8ZQAAYEbAX66CnL2mY+AQouNDY70ezHB12UtJYN1eMCv128opYpMWAACCVUF2tukIOIzYpIamI8DFXFv2bMdhZM8FNu5mO2cAAILVnm1bTEfAYSQ0bGQ6AlzMtWUvYFP23GD97n2V2oIWAADUL8dxlE3ZC3rxyY1kBwKmY8ClXFv2JCmFs/aC3ua9RaYjAACAP8BOnMEvrmEjOY5tOgZcyrVlz2tZjOy5wJa9hZy1BwBAEOLYBXdISE6W5XHtS3YY5tpHjsdjqWkiZS/Ybc5iZA8AgGCVuzPTdAQcRkJKE3k8nC2N6nFt2ZOkJgnRpiPgMLZmU/YAAAhG/vJyFeawG2ewS0hubDpCrbHt+tvHoapf67777pNlWQf96dKlS8Xlw4cP/83l11577UG3MX/+fI0YMUJJSUlq2LChRo0apaVLl1Zcvnnz5t/chmVZmjNnTs3u7CH46uyW60FyHEcvBLvi8oByisrUMJZ/KwAAgsm+vXtMR0AlxDZoYDpCrfF4LP3wv5XKzqzb3dqTm8Vp5OXdq/x53bt316RJkyr+7vMdXJWuuuoq3X///RV/j42Nrfj/goICnXjiiRo9erReeOEF+f1+3XvvvRo1apS2bdumiIiIiutOmjRJ3bv/f75Gjepux1VXl72YSK+iIzwqKWfRajDbtKdADVsnm44BAAB+5jiO9mzZZDoGDiMmIVFeX8Thr+gi2ZmFytpWYDrG7/L5fEpNTf3Dy2NjY//w8jVr1ig7O1v333+/WrZsKUm699571atXL23ZskUdOnSouG6jRo0O+XVqk6uncUpikxYX2LCnkOMXAAAIMjkZO0xHwGHENeTN8vq0fv16paWlqV27drrwwgu1devWgy4fP368GjdurB49eujOO+9UUdH/L1fq3LmzGjVqpNdff11lZWUqLi7W66+/rq5du6pNmzYH3c7o0aPVpEkTDR06VF988UWd3idXj+xJUrMG0dqeU2w6Bg4hfXdwvnsDAEC4siyLzVlcID6ZA9Xry6BBgzRu3Dh17txZmZmZ+te//qWjjz5aK1asUEJCgi644AK1bt1aaWlpWrZsmf7+979r7dq1+uSTTyRJCQkJmjJlik4//XQ98MADkqSOHTvqu+++q5gOGh8fryeeeEJDhgyRx+PRhAkTdPrpp+uzzz7T6NGj6+R+ub7stW4Up/mbc0zHwCGsyszn+AUAAIIMxy4Ev4TkRnIch9dR9eCkk06q+P9evXpp0KBBat26tT788ENdccUVuvrqqysu79mzp5o1a6YRI0YoPT1d7du3V3Fxsa644goNGTJE7733ngKBgB5//HGdcsopmj9/vmJiYtS4cWPdeuutFbczYMAAZWRk6LHHHqPs/Z7ygK1WybGHvyKMWpWRbzoCAAD4lZxMpnEGu/jkRrIDAXl9rn7J7kpJSUnq1KmTNmzY8LuXDxo0SJK0YcMGtW/fXu+++642b96s2bNny/PzuYjvvvuuGjZsqM8//1znnXfeH97ODz/8UDd3Qi5fs+exLLVpRNkLdnsLy5RbVGY6BgAA+Jm/rExFebmmY+AwEhqFzrELblNQUKD09HQ1a9bsdy9fsmSJJFVcXlRUJI/Hc9Ao7IG/2/Yfbya5ZMmSP/watcHVZc/rsdS+SbzpGKiEpdty2aQFAIAgkbOL9Xpu0DCtBaN69eSvf/2rpk6dqs2bN2vWrFkaM2aMvF6vzj//fKWnp+uBBx7QwoULtXnzZn3xxRe65JJLNGzYMPXq1UuSNHLkSOXk5Oj666/X6tWrtXLlSl122WXy+Xw69thjJUlvvvmm3nvvPa1Zs0Zr1qzRQw89pP/973+68cYb6+x+uf7R06ZRnOkIqIRlO/I0rFOK6RgAAIQ9x3GUuW6N6RiohORmzU1HqHXJzer+tXt1vsb27dt1/vnna+/evUpJSdHQoUM1Z84cpaSkqKSkRJMmTdLTTz+twsJCtWzZUmeeeab++c9/Vnx+ly5d9OWXX+pf//qXBg8eLI/Ho759+2rixIkHjdw98MAD2rJli3w+n7p06aIPPvhAZ511Vq3c799jOSEw3NLn/u+VW1RuOgYO4cQeqXrpov6mYwAAEPYcx9H3Lz+jFT/V3Toh1JwvKko3vzXBdIwqKykp0aZNm9S2bVtFR0cfdJltO/J46mezmfr8WvXtUN/jX3P1NM4DGN0Lfisz8kxHAAAA2n/sQub6taZj4DAapqaZjlDr6rN8hWrRq6qQKHvsyBn8tucUq6TMbzoGAABhr7y0VHt3bDMdA4eRFIJlD/XP9WWvPGCrTWPKXrBzHGllZj6btAAAYJDjONq5Yd3+X8wIag1Tm8kOBEzHgMu5vuxZ2n+wOoLfsu1M5QQAwLQda1eajoBKSEpN401y1Jjry57P61GHFI5fcIOVGfkHnT0CAADqF+v13CO5OccuoOZcX/YkqTUHq7vCkm25piMAABD2KHvu0DAEj11A/QuJspcUG6mEKN75CHYbdheooJRNWgAAMKUwN0fF+/JNx8BhRETHKDaxgekYCAEhUfYkqRWje64wOz2L+ecAABjgOI62rVpuOgYqoWFqs8NfCaiEkCl7TOV0h9npe01HAAAgbGWsXWU6AiqBYxdQW0Ki7PkDNgeru8TcTdls0gIAgAGWZSmD9Xqu0LBZGscuuMy4ceOUlJRkOsZvhETZk6T2TdiR0w1WZ+arpJwnLwAA6lsg4NeezZtMx0AlNGrRynSEOmHbdtB/rW3btunyyy9XWlqaIiMj1bp1a918883au/f/Z6e1adNGTz/9dC0lrVshsauJz+tRr+YsYnUD25EWbM7WkA6NGeEDAKCeOI6jPZs3yQ6wUZobpLbvKI/XazpGrfN4PPr62ceVvWNbnX6d5OYtdcqNf63y523cuFGDBw9Wp06d9N5776lt27ZauXKlbr/9dn377beaM2eOkpOT6yDxHysvL1dERES1Pz8kyp4ktU2JU6TXo7JA/b1jgOqZlb5XQzo0Nh0DAICwsmX5EtMRUAneiAg1aBq6G7Rk79im3ZvSTcf4Xddff70iIyP1/fffKyYmRpLUqlUr9e3bV+3bt9ddd92l1atXa8uWLRo7dqzGjh0rSQdtPvjdd9/plltu0bZt2zR06FC98cYbatbs//89X3vtNT3xxBPatGmT2rRpo5tuukl/+ctfJEmbN29W27Zt9f777+uFF17Q3Llz9dJLL+nPf/5zte9TyJQ9n8ejTqnxWrGD7YSDHev2AACoX5ZlacuyxaZjoBIatWgljydkVlq5RnZ2tr777js9+OCDFUXvgNTUVF144YX64IMPtH79evXp00dXX321rrrqqoOuV1RUpMcff1xvv/22PB6PLrroIv31r3/V+PHjJUnjx4/XPffco+eee059+/bV4sWLddVVVykuLk6XXnppxe3ccccdeuKJJ9S3b19FR0fX6H6FTNlzHEfd0xpQ9lxg+fY8lQdsRXh5IgMAoD4E/H5lrFttOgYqoUnrdnIchzfG69n69evlOI66du36u5d37dpVOTk5CgQC8nq9SkhIUGpq6kHXKS8v10svvaT27dtLkm644Qbdf//9FZffe++9euKJJ3TGGWdIktq2batVq1bp5ZdfPqjs3XLLLRXXqamQKXt+21G3ZommY6ASygK2lmzL1RGtG/JEBgBAHXMcR9vXrFSgvNx0FFRCSuu2sgMBeX0h8zLdVWpyHnRsbGxF0ZOkZs2aaffu3ZKkwsJCpaen64orrjhoRNDv96tBg4P3HjniiCOqneHXQuZRFOH1qFcLNmlxi1npe3VE64amYwAAEBa2LF1kOgIqqUnb9iG5OUuw69ChgyzL0urVqzVmzJjfXL569Wo1bNhQKSkpf3gbv95IxbKsivJYUFAgSXr11Vc1aNCgg67n/dW/d1xc7R0pF1Lz6Lo2S5SHgSJXmLZuD6N6AADUA8uy2JzFRZq0actrJAMaNWqkkSNH6oUXXlBxcfFBl+3cuVPjx4/XueeeK8uyFBkZqUAVz0Fs2rSp0tLStHHjRnXo0OGgP23btq3Nu3KQkCp70RFeteZwdVdYvDVHhaVs/wwAQF0rKynW7s0bTcdAJSQ0SlFkTKzpGGHrueeeU2lpqUaNGqVp06Zp27ZtmjhxokaOHKnmzZvrwQcflLT/nL1p06Zpx44dysrKqvTt/+tf/9LDDz+sZ555RuvWrdPy5cv1xhtv6Mknn6yruxQ60zgP6J6WqE1ZhaZj4DBsR5q0ZpdG90rj3SsAAOqI4zjatGSRVIN1SKg/Ka3rboQnWCQ3bxm0X6Njx45asGCB7r33Xp1zzjnKzs5WamqqTj/9dN17770VZ+zdf//9uuaaa9S+fXuVlpZWep3flVdeqdjYWD322GO6/fbbFRcXp549e+qWW26pVt7KsJyarEIMMuUBW69N36j/TFxrOgoqYXTvND1zfl/TMQAACFmO4+iHV5/T8h+/Mx0FlTDojHN11FkXuHrNXklJiTZt2qS2bdv+5tgA27br7ViJ+vxa9e1Q3+NfC6nvgNdjqUdzNmlxi6nr9si2Q+a9BgAAgg7n67lLk9btTEeoU/VZvkK16FVVSH0XPJalnpQ918grLtfyHXk12uIWAAD8PsdxlJ2xXfl7dpuOgkpq2q6Dq0f1EHxCquxJUlJspJokRJmOgUr6buVO0xEAAAhZ6+bMNB0BlRQZE6PElCamYyDEhFzZk6TuaYzuucXkNbvZoAUAgDpgWZY2LppnOgYqqVmHzrwmQq0LubLnD9jqlpZoOgYqac3OfdpbUGo6BgAAIaekYJ8yN6wzHQOV1KxTF9lVPLsNOJyQK3uWJfWg7LnKxBU7WbcHAEAtchxH6+fN4sgFF2neuVtIjezZtm06QsiqyuvmkDtnz+vx6Ig2yaZjoAomr92tC49sbToGAAAhw7IsrZ8323QMVJZlKa1TV1khsINkZGSkPB6PMjIylJKSosjIyJAqsaY5jqM9e/bIsixFREQc9vohV/YkKSUhSi0axmh7TrHpKKiEmRuyVOoPKMrH7lMAANQGf1mZtq5YajoGKik5rYUiY2JMx6gVHo9Hbdu2VWZmpjIyMkzHCUmWZalFixbyVmLn1pAse5I0sG2ytufsMB0DlVBSbuu7FTt1au803vkBAKCGHMdR+sK5CpSXm46CSmreuascxwmZ10GRkZFq1aqV/H6/AqxDrHURERGVKnpSiJa98oCtAW2S9ckiyp5bfLE0Q6P7NDcdAwAA12MKp/s067h/cxavL3Remh+YZliZqYaoO+6fGPw7IrweDenQ2HQMVMG0dVkqLuOdHwAAaspfXqb0hXNNx0AVtOjWI6SKHoJHSJY9SWqVHKuUeA5Xd4uygK3Pl+5gV04AAGrAcRytnzNL/lKONXKL6Lh4NUxNMx0DISpky54kDWjb0HQEVMEXSzJCZq46AAAmWJal1TOmmI6BKmjWsYvpCAhhIVv2Dqzbg3vM2bhXecUsJgcAoLpKi4u0Zfli0zFQBc06dVHA7zcdAyEqZMse6/bcx3akTxZtZyonAADV4DiOVk//STa7H7pKiy7d5ankzopAVYVs2ZOkDk3ilRjDYlc3+XIpUzkBAKgOy7K0ZsZU0zFQBZbHo9QOnXjtgzoT0mXPY1k6ojVTOd1k0dZc7corMR0DAADXKczN0Y51q03HQBWktu+oiCg2FETdCemyVx6wNbAtZc9tJjCVEwCAKnEcRyumTJL4/ekqrXr2Ydot6lRIlz2fx9Lg9o1Mx0AVfc6unAAAVMn+KZxTTMdAFbXp3U+WJ6RfjsOwkH50WZal7mmJiolg0aubrN21T6sy8hjdAwCgEhzH0a5N6cratsV0FFSBLypKaR278AY36lRIlz1J8nk86tsqyXQMVNG4WfzCAgCgMizL0uKJX5qOgSpiF07Uh5Ave/6ArUGs23OdL5dmqLTcNh0DAICgV15aqrWzppuOgSpq3asv5+uhzoV82fN4LB3Juj3XKS4P6MOF25jKCQDAITiOo+WTv5O/rNR0FFRRmz795fVxRBjqVuiXPctS/1YNFRfJMLnbvDt3K/PYAQA4BMuytGzSRNMxUEWxDZLUuEUr0zEQBkK+7EmSz+vR0I4ppmOgitbs3KcVO9ioBQCA3+M4jjLXr9Xe7VtNR0EVterey3QEhImwKHvlAVvHdWliOgaqYdyszYzuAQDwO9iYxb1a9ezDej3Ui7AoexFej0Z2ayI6g/t8tSxDxWU8GQIA8GtlJcVaN3em6RiohrZ9j2C9HupFWJQ9SUqOi1KPtAamY6CKSsptvT+fjVoAAPglx3G0bNJEBcrLTUdBFSWlpim+ITvFo36ETdnz20zldCs2agEA4GBszOJerXv1kWNzvBTqR9iUPY9laWS3pqZjoBrW7y7Qoq05jO4BAKD9o3rbV69QTuYO01FQDW37HMFrGtSbsCp7PZo3UOP4SNNRUA0vT93I6B4AADqwMctXpmOgGnyRUWrdq488Xo4EQ/0Im7In7X8nbHhnpnK60Q+rdiozt5h3wgAAYa+koEAb5s8xHQPV0KpHb/kiGHhA/QmrshewHdbtuZTtSM9O3mA6BgAARjmOo0Xffi47wE7VbtS+/0COXEC9Cquy5/N6NLxTinwepgO60YRF21VQyhMkACB8+ctKteibL0zHQHVYljoMOJIjF1CvwqrsSVJslE8D2rDdrRuV+m29PG0jUzkBAGHJcRwt+OozlRYVmo6Camjatr1iGySZjoEwE3Zlrzxg61imcrrW27O3qDzAdsUAgPATKC/Xom8+Nx0D1dS+/0DZgYDpGAgzYVf2IrwencARDK6VV1yu8XO3MroHAAgr+9fqfaGSgn2mo6CaOg4aKssTdi+9YVhYPuLaNI5Tq+RY0zFQTa/P2CSbrgcACCN2wK8FX31qOgaqqUGTpmrcshXHSKHehWXZsx125XSz7TnF+np5JqN7AICw4DiOFk/8WsX5eaajoJo6DDxKts0yFNS/sCx7jiOd3DPVdAzUwMtT03l3DAAQFhw7oAVfTjAdAzXQaeBR4lULTAjLsuf1WDqiTbKaJESZjoJqWpmRr1kbshjdAwCENMdxtHTSRBXm5piOgmqKbZCkZp26sF4PRoT1o+5PvZqZjoAaePz7tYzuAQBCmmPbmvfZR6ZjoAba9x9kOgLCWFiXvTH9WpiOgBpYtDVXU9fuZnQPABCSHMfRyik/qiB7r+koqIFOg4fKcVivBzPCtux5LEs9mzdgV06X+89ERvcAAKHJcRzN+fR90zFQA7ENktSqR295PF7TURCmwrbsSVLAdpjK6XKrMvM1cQU7cwIAQovjOFo17Ufl79ltOgpqoNORQ9mYBUaFddnzWNIZTOV0vce+Wyu6HgAglDiOozmffGA6Bmqo27BjTUdAmAvrsmdZljo0iVenpvGmo6AG0vcU6pNF2xndAwCEBMdxtGbGFOXt2mk6CmqgQZOmatahM7twwqiwf/T5bVujezc3HQM19PSP62XblD0AQGiY++mHpiOghroMOUZ2IGA6BsJc2Jc9n8ejMX0pe263PadY4+dtZXQPAOBqjuNo7ezpys7YbjoKaqj7MSMY1YNxPAIlNW8Yo94tGpiOgRp6bvIG+QOUPQCAezmOo9kfv2c6BmoopXVbNWzWnB3DYRxlT5I/YGt0nzTTMVBDu/eV6n8zNzG6BwBwJcdxtPjbL5S9Y5vpKKghpnAiWFD2JPm8Hp3ep7k8vPniei9NTVdxWYDCBwBwFcdxVFpUqFkfvWs6CmrKstRt2HHyeDlbD+ZR9n7WKD5Kg9o2Mh0DNZRTVK5Hv1trOgYAAFViWZamvPWayoqLTEdBDTXv0k3xDZNNxwAkUfYq+AO2Tu3NVM5Q8M6cLdqWXcToHgDAFRzH0Z4tm7Ry6o+mo6AWdB0yXAG/33QMQBJlr4LP69GfejdThJe5nG7ntx3d+elyFkUDAFzju5efkXiT0vU8Xp+6DBkmr89nOgogibJ3kMToCB3buYnpGKgFMzfs1Y+rdzK6BwAIao7jaOXUH7Urfb3pKKgF7foeoajYONMxgAqUvV/wB2xdOKiV6RioJfd9uUoBDloHAAQxf1mZpo1/w3QM1JJeI09kF04EFcreL/i8Hh3dKUUtGsaYjoJasC27WC9OTWd0DwAQlBzH0fR3x6k4P890FNSChEYpatO7P7twIqhQ9n7FdhydO6Cl6RioJS/8lK7swjIKHwAgqDiOo9xdO7Xk+69NR0Et6XHsSDm2bToGcBDK3q/4PB5dMLCVfBy6FxKKywP615er2KwFABBULMvS9y8/QzkIEZblUa/jT2RUD0GHsvc7GsVHaUTXpqZjoJZ8sTRDi7fmMLoHAAgKjuNo3dxZ2r5quekoqCVt+vTjbD0EJcre7/Dbti4+ko1aQsk/P1vBjtYAgKBg+/2a8uYrpmOgFvUeeRIbsyAoUfZ+h8/j0dCOKWqZzEYtoWJlRr5en7mJ0T0AgFGO42j2hPe0b2+W6SioJfENG6lt3wFM4URQouz9Ab9t67wBjO6Fkie+X6vd+aUUPgCAEY7jqCBnrxZ89anpKKhF3Y89XhKvLRCcKHt/wOfx6Hw2agkpJeW2bvlgCZu1AACMsCxLk159XoHyctNRUFssS72PP0mWxUtqBCcemYeQHBepkd3YqCWUzN64V+/P28roHgCgXjmOo01LFmjjovmmo6AWte7VVwmNGvNGMoIWZe8Q9m/U0tp0DNSyB79erdyicgofAKBeOI4jf2mpfnjledNRUMt6jziRjVkQ1Ch7h+DzeHRUh8Zq3SjWdBTUon2lfv3146W8CwcAqBeWZen7V57Vvr17TEdBLYptkKT2RwxiYxYENcreYfgDts5no5aQ8+Pq3fpiaQajewCAOuU4jtbMmqY1M6eajoJa1uv4EyXeOEaQo+wdhs/r0XkDWyrCyw9zqLnvi5UqKPFT+AAAdcJxHBXn52vSa0zfDDVen0/9Thotj4eX0ghuPEIrISk2UqO6p5qOgVqWXVimuz5bwXROAECdsCxLX/33PyotLDQdBbWsy5BjFJOQaDoGcFiUvUoI2I6uGNrWdAzUgS+WZujH1bsY3QMA1CrHcbTgq0+1beUy01FQBwaMPlO2bZuOARwWZa8SvB5LfVs1VL9WDU1HQR24Y8Jy5RcznRMAUDscx1HerkzNeP8t01FQB1r16K1GLVoxhROuwKO0kvwBW9cNb2c6BurAnoJS3fz+YqZzAgBqhWPb+vyJhzg8PUT1/9MYjluAa1D2Ksnn9WhE16ZqnxJnOgrqwJR1e/T6jI2M7gEAamzau+OUtXWz6RioAw2bNVe7vkdw3AJcg7JXBbbt6KqjGd0LVf/5dq027Cmg8AEAqsVxHO1Yt1oLv/7MdBTUkX4nj2ZUD65C2asCn9ejM/u3UEp8lOkoqANlAVvXvr1Q5QGbwgcAqBLHceQvK9PXT/9H4ndISIqOT1CPY0cyqgdXoexVkceS/jykjekYqCPpewp19+crWb8HAKgSy7L0/Uv/1b69WaajoI70Ov5Eebw+0zGAKqHsVZHX49Gfj2qjuEje1QlVH8zfpu9WZDK6BwCoFMdxtGbmVK2ZNc10FNQRj9enfiedxpvBcB3KXjXERHp13sBWpmOgDt0+YZmyCkopfACAQ3IcR0X5eZr0+gumo6AOdRo8VHFJSZQ9uA5lrxosSdcMayefhx/4UJVf7Ndfxi9i2QUA4JAsy9LX/31UpYWFpqOgDnGIOtyKslcNlmWpSWK0Tu2dZjoK6tD8zTl6ZvJ6RvcAAL/LcRzN/2KCtq1cZjoK6lDbPkeoSeu2HKIOV+JRW00B29Ffhrc3HQN17Jkf12v6+j0UPgDAQRzH0daVyzT9vTdNR0EdO+qcCzluAa5F2asmr8dSx6YJOqZTiukoqEO2I93w7mLtzCuh8AEAJO0vevv2ZunLJx+Sw9S+kNa6Zx+ltu/IcQtwLcpeDfgDtq5jdC/k5Zf4dcn/5nH+HgBg/3l65WWa8ODdrNMLA4PPvoBRPbgaZa8GfF6PjmzXSD2bNzAdBXVs/e4C3fT+EnbhAoBw5zj64omHlJ2x3XQS1LEWXXuoeedujOrB1Sh7NeQP2KzdCxMTV+zUc5M3MLoHAGFs6vg3tHnJQtMxUA+OOpu1enA/yl4N+bwendSzmbo2SzAdBfXgyR/WsmELAIQhx3G0atpPWvjVp6ajoB6kde6qlt17MqoH16Ps1QJ/wNZfT+hsOgbqge1IN7y3WJls2AIAYcNxHO3ZvFHfv/KM6SioJ4PPYq0eQgNlrxb4vB6N6NpUfVsmmY6CepBf7Nef35inMjZsAYCQ5ziOivfla8LD9ypQXm46DupBavtOatOrL6N6CAmUvVriD9j624mM7oWLdbsKdDMbtgBAyLMDAU146B4V5eWajoJ6Mvis8xnVQ8ig7NUSn9ejwe0ba3D7RqajoJ5MXLFTT3y/1nQMAEAd+va5J7R7U7rpGKgnTdq0U7t+AxjVQ8ig7NUif8DWHSd2MR0D9ejZyRv0/rytTOcEgBDjOI5mT3hfa2dPNx0F9ejIM8+XHfCbjgHUGspeLfJ5PerdMknHdWliOgrq0V2frdCM9VkUPgAIEY7jaOOi+Zr10XjTUVCPUtt3UseBg+Xx+kxHAWoNZa+WBWxHfz+xi1jKFT4CtqNr3lmotbv2UfgAwOUcx1FO5g59/d9HJZ7Tw8oxF13OWj2EHMpeLfN6LHVOTdDJPZqZjoJ6VFQW0EWvzdWufaUUPgBwKcdxVFZUqAkP3aPy0hLTcVCP2vTupxbderBWDyGHslcHAraj20/sLK+H4b1wklVQpgtemaOCUj+FDwBcxnEc2X6/Pn30AeXv2W06DuqTZemYi69gVA8hibJXB7weS20axWlM3+amo6Cebcwq1KX/my+/7VD4AMAlHMeRbdv67LEHtGPNStNxUM+6DjlGjVu2ZlQPIYmyV0dsx9GtIzspwsvoXrhZtDVHN7y7SFQ9AAh+jrP/zbkvn3xIm5cuMh0H9czr8+noC/4sx7ZNRwHqBGWvjngsS80aROvcAa1MR4EB363cpfu+4N1hAGZMmzZNp556qtLS0mRZlj777LNDXj8zM1MXXHCBOnXqJI/Ho1tuueU31/nhhx/UqVMnJSYm6uKLL1ZZWVnFZXl5eerUqZO2bNlSy/ekHjiOvnnmMaUvmGs6CQzofcLJik9uJMvDS2KEJh7ZdeyW4zsqOoJvczh6a/YWPTd5g+kYAMJQYWGhevfureeff75S1y8tLVVKSor++c9/qnfv3r+53LZtXXDBBbr22mv1f+3dd3hUZd7G8e85M+k9IQmBAIHQQpMqHaQqgoiANGliVwQVWLAX3NXFhq6u3de1iwq7dhSVDtIVFaT3ElqAkIRkZs77xxAEQaXM5GQm9+e6coUkM8/zi2bmzD1PW7BgAUuWLOGll146/vMJEyZw4403UqVKFZ/9DiXly+cn6yy9Mio0IpKWfQfZXYaIX+kgET8yDIPEqFCGtszgpdkb7C5HbPD4V78SGepgRJuqdpciImVIt27d6Nat2xnfPiMjg6effhqA11577ZSf7927l71793LzzTcTHh5Oz549WbVqFQDz589n8eLFPPvss74pvgR9/fJz/DL7W7vLEJs069mb0IhIDJ2XJUFMQ05+ZgCjO9UgKSrU7lLEJg99+gvvfr9ZG7aISMBKTk4mLS2Nr776iry8PObMmUODBg0oKiripptu4sUXX8QRYJtbfPfGK/w44wu7yxCbRMUn0PSy3piavilBTn/hfmYYBmEhJuMurmV3KWKju/77E/9bsUOBT0QCkmEYTJkyhYkTJ1K3bl0aNWrEiBEjePTRR+nQoQPh4eG0bt2aWrVqBcQI35z33mDZZ/+1uwyxUcu+gzDNwHqDQuRcaBpnCXCaJv2aVeKt7zfz0/ZDdpcjNrAsGPPBD4Q7TS6uV15TRkQk4LRp04bFixcf/3rNmjW88cYbLF++nHbt2jF69Gi6detGvXr1aNeuHQ0aNLCx2j+2cNr7LJo2xe4yxEYJaRWp3+lijepJmaC/8hLi8VhMvLye3WWIjdwei5HvLmfGL7s1wiciAe+GG27giSeewOPxsHz5cq688kpSUlJo3749s2bNsru801r62X+Z996bdpchNus4/Hrvu7AiZYDCXglxOkwaVU7g8oYV7C5FbOTyWNz8zjK+WaXAJyJn51yPUwC44oorfHqcwquvvkpiYiI9e/bE7XYDUFRUdPxz8fdKC8uy+GHGF8x84xW7SxGbVWt8IRkNm+gAdSkzFPZKkMeyuLd7HSJD9QRTlhW5LW56exnfrs5W4BORM3Y2xynk5uayfPny419XqFCBPXv2sGXLFgDuvPNOhgwZctJxCnPnzuW+++4jNzeXPXv2cO2119KrV69TjlPIzs7m4Ycf5l//+hcACQkJZGVlMXnyZBYsWMA333xD69atffibnx/Lsvhl9rfMeOXfdpciNnOEhNBpxI14dIC6lCGGpVebJcrtsXhh1noem/6r3aWIzUIcBi8ObkKH2ilawyciZ8UwDKZNm0avXr1O+/OZM2fSoUOHU74/bNgwXn/9dYYPH87atWuZP38++fn5hIeHn/Z5qEqVKmzatOmk7w0cOJDWrVszcuTI499btGgRw4YNIzs7m9GjR3Pfffed1+/nK5Zlseb7eXw2eRKWpRf4ZV3zK/rRut9gHaAuZYrCng2K3B46PTGLLfvz7C5FbBbiMHhmQCMu0aYtInIW/irs/d5FF11Ew4YNmTx58vHvWZZFxYoVeeGFF+jcuTOdO3dm2LBhjBgxgqZNm/Laa6/RpEkT//wCJcCyLNYv+Z5PnnoETymbViolLyapHCMmv4QzVEdhSdmitzZsYAD39ahjdxlSChS5LW55ZxnvL95qdykiUsYE23EKJyoOep9OflRBTwBoP+RardOTMklHL9jA6TDpXCeVtjXKMWftXrvLEZt5LJgwdSUH8gq56aLqdpcjImVIsByn8Hs/zviSb159XlM3BYBKdetTq2Ubu8sQsYVG9mzi9lg82LMuTlNT98Trn1/+yiOfr7K7DBEpwwLxOIXfmzflLWa88pyCngBgmCadrrlZI7xSZins2cRhGlQtF8XQllX++sZSZrw4ewMTPvoRj2Vpp04RKVGBdpzCiSzLwvJ4+Oqlf7Hwo/fsLkdKkYZdu5NYIV1TOKXMUtiz2ZiutUiK0mJh+c17i7cy8p1leDwKfCLym9zcXFasWMGKFSsA2LhxIytWrDjpOIWhQ4eedJ/i2xcfp7BixQp++eWXU9oOtOMUTmRZFh63m4+f+Acrv5ludzlSikTExtFmwNC/vqFIENNunDZzeTx8uGQbE6autLsUKWXa1ijHK8OaEuowtVOnSBCYPXs2jz32GEuXLmXnzp1ntJvm0aNHeeihh3jrrbfYsWMHLpfrlNvExMQAkJycTHp6+vHplgcPHiQ+Pv6U2wf6cQonsiyLosKjTP37fWz/9dQQK2Vb1xtGUbd9J43qSZmmkT2bOU2Tfs0q0SA9zu5SpJSZs3YvA19aSF6hWyN8IkHgbA5FL9avXz+++eYbXn311ePn4s2dOxfLsnC73ZQrV44HHniABQsWEBoaypVXXnn8vhMmTOCJJ57wTnE84eP3QQ/g3XffPSnoAVx44YWsWrWKffv2ldqgl3foIO/cdYeCnpyifGZN6nXooqAnZZ5G9koBl9vDxr1HuPSZORS59b9DTlYzNZo3r2lOSkyYRvhEgsSZnJP35ZdfMmDAADZs2EBiYuIpP8/OziY1NfX4oejjx48nNzeX5557jvnz5zNq1Ci+//57HEH4YteyLHJ27+KDh+7k8D7tai0nMx1Ohk56hoS0igp7UuZpZK8UcDpMMlOiubF9pt2lSCm0Zncu3Z+Zw887DmmET6QM+fjjj2natCmTJk2iYsWK1KxZk7Fjx5Kfnw94p22mpaXx1VdfkZeXx5w5c2jQoAFFRUXcdNNNvPjii0Eb9HatX8s7d92uoCen1ezyPiRWrKSgJ4LCXqlhGgajO9WgRkq03aVIKbQ3t5A+z8/n0x932F2KiJSQDRs2MHfuXH766SemTZvG5MmT+fDDD7n55puB4D4U/Y9YlsWG5YuZ8sAECo7k2l2OlEKJFdNp2WegZsKIHKNpnKWIy+3h552HuOK5eXj0f0X+wMiO1RnbtRaWZeliJhKgzmQaZ9euXZkzZw67du0iLs67rnvq1Kn07duXI0eOEBERccp91qxZQ/fu3U97KPqMGTMC5lD03yt+vlvy6TRmv/1/WB6doSenYRgMfOgxymdWx3Q47a5GpFTQyF4p4nSYNKgYx/BWGXaXIqXYs9+u45a3l+HS0QwiQS0tLY2KFSseD3oAWVlZWJbFtm3bTnufYDgU/fcsy8Lj8fDlv59i1puvKujJH2rY5VIq1KytoCdyAoW9UsYwDP52SW0qJZ76jq1Isc9W7qTv8/PJyStS4BMJUq1bt2bHjh3k5v42XXHNmjWYpkl6evoptw/kQ9H/iGVZHD2Sy/v3j+fnWd/YXY6UYjFJybQbPELXRJHfUdgrhZymwaQ+F9hdhpRyP2w7yKXPzGFt9mFd3EQCwNkeij5o0CCSkpK4+uqr+eWXX5g9ezbjxo1jxIgRp0zhDORD0f+IZVns3bqZ/4wbyc61q+0uR0q5LtffgsPp1PIGkd9R2CuFnA6TlplJ9G9Wye5SpJTbebCAXs/N55tV2Qp8IqXckiVLaNSoEY0aNQLgjjvuoFGjRsfPsNu5c+fx4AcQHR3N119/TU5ODk2bNuWqq67isssu45lnnjml7dGjRzNmzBgqVKhw/Huvv/467733Hj169GDcuHE0a9bMz7+hb/26YA7v3D2G3P377C5FSrnardtTtWFT7b4pchraoKWUsiyLvEI3HZ+Yye5DR+0uR0o5w4BbO1bnts41MUDvbIpIQCreiGXOu/9h0X8/sLscCQARMbGMmPwiYZFRGKbGMER+T4+KUsowDMKcJn+/or7dpUgAsCx45pt1DHn1e3KPujTKJyIBx7Isio4WMPXRBxT05IxdNOw6QiMiFfRE/oAeGaWY02HSOSuVHg3S7C5FAsS8dfvo/OQsfth2UIFPRALG8fV5Y29h4/IldpcjASLjgsbUadtB0zdF/oSmcZZyHsviUH4RHR6fyYG8IrvLkQDhMA3Gda3FjRdl6jw+ESm1ip+f1i1eyKdP/xN3ka5zcmbCoqIY/sTzRMbFY2pUT+QP6dFRypmGQXS4k/svq2t3KRJA3B6LR79czbX/WUx+oVujfCJS6liWhdvloTC/kLQatQgN15FDcuY6X3sLkbFxCnoif0GPkADgNE16NapIx9opdpciAWbGqmy6Tp7Nqp2HFPhEpNSwLIsDu/N4/+HFTHtyBeHRMXQfNc6725TIX6jduj21W7XT9E2RM6CwFyDcHovHr7yApKhQu0uRALPtQD69npvPmws3Ayj0iYhtip9/fp6zgyl/X0zO7jz2bs3l+483Ubl+Q5pf0c/mCqW0i0lKpst1I7E8HrtLEQkICnsBwmEaxIY7eaKfDluXs1fo9nDf/37mlreXkVeo3TpFpORZlkVRgZvPX/iRWe/8irvotxfry7/awo61ObS+8ioq1W1gY5VSqhkGl44cgzM0RLtvipwhPVICiNNhclGtFK5unWF3KRKgPlu5k45PzGL+ur12lyLyl7Zv387gwYNJSkoiIiKC+vXrs2TJn+/U+Nxzz5GVlUVERAS1atXijTfeOOnnX3/9NTVr1iQ2NpYhQ4ZQWFh4/GcHDx6kZs2abN682S+/T1m3c20O7zy4kI0rTv/88/Hk5RTkFXHZbeOJik8o4eokEDTt3ouKWXUxHU67SxEJGAp7AeiuS7OokxZrdxkSoHYfOspVry7irqkrKXRp8xYpnQ4cOEDr1q0JCQnhiy++4JdffuGJJ54gIeGPQ8Dzzz/PnXfeyQMPPMDPP//Mgw8+yC233MInn3wCgMfjYdCgQdx4440sWLCAJUuW8NJLLx2//4QJE7jxxhupUqWK33+/ssKyLNxuD3M/WMu0p5ZzJKfwD2/r8cC0J5YTGhlFj9smaORGTlKucgZtBg7T7tIiZ0lHLwQgl9vD1gP5XPr0HPKL3HaXIwGsSlIk/xrQiPrpcbqASqkyYcIE5s2bx5w5c874Pq1ataJ169Y89thjx783ZswYvv/+e+bOnUt2djapqank5+cTHh7O+PHjyc3N5bnnnmP+/PmMGjWK77//Hoc2ffAJy7LI2Z3Hly/9xP4dR874fvUuSqdd/xp8P+195r3/lh8rlEDhCAlhyD+fIaF8BW3KInKW9LZZAHI6TConRnLfZXXsLkUC3OZ9efT69zwe/WI1LrdHo3xSanz88cc0bdqUK6+8kpSUFBo1asTLL7/8p/c5evQo4eHhJ30vIiKCRYsWUVRURHJyMmlpaXz11Vfk5eUxZ84cGjRoQFFRETfddBMvvviigp4PWJaF5bFY/tUW3nt40VkFPYCfZm5jyy/7adF7ABkNm/ipSgkkbQYMIzGtooKeyDlQ2AtQDtNg4IWVuaReebtLkQDnseDF2Rvo8a+5rN+Tq8AnpcKGDRt4/vnnqVGjBtOnT+emm25i1KhR/Oc///nD+1x88cW88sorLF26FMuyWLJkCa+88gpFRUXs3bsXwzCYMmUKEydOpG7dujRq1IgRI0bw6KOP0qFDB8LDw2ndujW1atXi2WefLcHfNnhYlsWhPfl8OGkJC6atx+M6t+eTT5/7gbyDBXQfNY6YpHI+rlICSeV6F9C0Ry9N6xU5R5rGGcA8lsWRoy66PjWbnQcL7C5HgkCIw2B0p5rcfFEmhoGmdoptQkNDadq0KfPnzz/+vVGjRrF48WIWLFhw2vvk5+dzyy238Oabb2JZFqmpqQwePJhJkyaxa9cuUlNTT7nPmjVr6N69O8uXL6ddu3aMHj2abt26Ua9ePWbMmEGDBtoZ8kxYlnX8+WLKPxazZ8vh824zNjmcgfddyJ5N63nv/r/hcWvZQlkTFhXF1U++QERMrEb1RM6R3iYJYKZhEBHi4JmBjTD1mlx8oMht8fhXv9L7+fmsy9Yon9gnLS2NOnVOnqqelZXFli1b/vA+ERERvPbaa+Tl5bFp0ya2bNlCRkYGMTExJCcnn/Y+N9xwA0888QQej4fly5cfnzbavn17Zs2a5dPfKVhZlsXR9evZNHgI7oKjdBhcG8MHF6VDewqY/d5aymfWoO3AYT6oVAJN1xtGKeiJnCeFvQDndJg0rZLALR2q212KBJEVW3O45Ok5PPzZKu3YKbZo3bo1v/7660nfW7NmzRntlBkSEkJ6ejoOh4P33nuPHj16YJ5mCtirr75KYmIiPXv2xH1s1KioqOj4Z7dGkv6UZVlYLhd7Jj/Nxl5XkL9kCbsfeohylaJp2LmST/pYNW8nG37YS9PLepPZtLlP2pTA0OiSy6jZvLWCnsh50jTOIOH2WFz5wgKWbTlgdykSZNLiwnmoZ1261C1/0lQtEX9avHgxrVq14sEHH6Rfv34sWrSI6667jpdeeomrrroKgDvvvJPt27cfP0tvzZo1LFq0iObNm3PgwAGefPJJvv76a5YuXUpGRsZJ7WdnZ9O8eXPmzZtHhQoVAKhTpw79+/ena9eudO3alW+//ZZmzZqV6O8dCIqfB44sXsyue++jcNOmk36eMeV9QuvU472Ji8jZneeTPoc90oqQcA9vjLuVQ3t2+6RNKb3KV6/JwIceU9AT8QGN7AUNi+euakRsuA4aFd/aebCA695cyojXF7Mn96hG+aRENGvWjGnTpvHuu+9Sr149Jk6cyOTJk48HPYCdO3eeNK3T7XbzxBNPcMEFF9ClSxcKCgqYP3/+KUEPYPTo0YwZM+Z40AN4/fXXj48Ejhs3TkHvNCzLwn3oMNvHjmXLkKGnBD2ALddeB24XnYdn4av3hj6atBSHM5SeY+7C4dR1LpiFR8fQc8zddpchEjQ0shdEXB4PX/60i5HvLLe7FAlSESEObu1YnRvaZ2JqAxeRMsOyLLAsDrz7LnuefgbPoUN/evv4gQMof999zP1gLT9+u80nNdS8MJVOw7P44avP+Pb/XvRJm1LKGAa9J9xPlfqNNKon4iMa2QsiTtOkR4MKXNkk3e5SJEjlF7mZNP1XLpk8m+VbcwA00icSxIof3wU//8ymvleye+LDfxn0AHLefY+jv/5KyyuqE1suwie1rFm0m3VL9njXcrVo45M2pXS5sGcfqjZsqqAn4kMa2QsylmVR5La44t/z+HnHX1+QRc6VYUDfxunc3T2LuIgQjfKJBBnLsnAfPEj2pEkcnPZfOMuXC2ZiItVnzmT35iNMe3IZ+OjVxpCHWxARbfLmhFEc2LnDN42K7dKz6tHvvn/oPD0RH9MjKsgYhoFpwCvDmpIQGWJ3ORLELAs+WLqNVo9+y9PfrNWunSJBovhxvO/ll1nfqRMHp04766AH4Nm/n71PPUWFGvHUbVvRZ/V9NGkZhumg55i7cYaE+qxdsU9kXDyX3T5B1xARP1DYC0JOh0lydBj/vqoJDh3AJ36WV+hm8oy1tPnnd0xZshWPx9IFWyQAFT9uf9n3CwAFq1bjOXJ+u2nu/7//o2Ddelr3rU5MYvh51wiQd6iQr15bRWLFSnS8+gaftCn2MQyTHqP/Rnh0jKZviviBwl6QcjpMmldLZEK32naXImVE9uGjjP9oJZc8PZs5a/cAWs8nEgiKH6c/7vmRQZ8NYsBnA9h7ZA+p4/+GEXr+I2dbR4zANCw6DPHd9Wjjir2sXrCL+p0upk67jj5rV0pey74DSK9TX0FPxE8U9oKYaRhc17YaPS+o8Nc3FvGRNbtzGfraYga9vJC12YcBhT6R0siyvKPwO3N3ctt3tzH4i8Gs3LsSgLvm340zJYXE4cPOux9Xdjb7nnuOSlmJZLVKO+/2in335mpydh+hy3UjSUqv7LN2peRUadCIFn0Gas23iB9pg5Yg57EsXNqwRWxiGHBFo4rc1a02SdFhx76ni7qInYov+zlHc3h2xbNMXTsVl8d1yu2m9JhCrcgM1nXpinvv3vPut9r0LzFSK/LOAws5klN43u0BhEc6GfpISw7vy+atCaMpOlrgk3bF/+JSUhn86NOERkRialMWEb/RoyvImdqwRWxkWTB12XZa//M7/vH5ag7lF2mUT8RGlmVxuPAwkxZPosuHXZjy65TTBj2A27+7HUJCSB492id9bxlxDQ6nwUVX+W46Z0Geiy9f+pn41PJ0uX6kz9oV/woJj+CKCQ8QEh6uoCfiZ3qElQHasEXsdtTl4eU5G2j+yDc89Okv5OQVHp9CJiL+VfxYO1J0hKeWPkXnDzvz1qq3OOo++qf3235kOzO2fUt8n96E1ap13nW4tm9n/yuvkFG/HDUvTD3v9opt+Xk/P83eSVabi6jf8WKftSt+Yhh0HzWWhPIVcDicdlcjEvQ0jbMM8VgWr87dyN8/W2V3KVLGhTlNrmxaids71yAxyrsBhKZ3ivhW8eX9SNERXl75Mu+tfo8819ntrhluhjOv/xyKlv/IlmHnv34PIPObb7ASUnjngYXkHfLNdE6AAfddSHxKGG/fdQd7Nm/0WbviW637D6H5Ff30nC9SQjSyV4ZowxYpLY66PLy1cDMtHvmGO6euJPuwd4RB7z2JnL/ikbxDhYd4fMnjdPygI6/99NpZBz2AAk8Br/z8GlHNLyS6Qwef1LdlxAhCQg3aDazpk/aKTZ20DLcLLh97N6ERET5tW3yjdqt2tOjdX0FPpARpZK+M8VgWRW4PVzw3n192asMWKR2cpkGvRhW5vXMNKiZEYlmWXgyInKXix83evL28vPJlPlr70V9O1TxTc/rNJnJvLusv7Q5FRefdXsqECSQNH8aXL61k/bI9PqjQq2KtBHqOasC6xQv55KlHfNaunL/UatUZ+NBjmE4HhqGxBpGSorBXBrncHvbkHuXSp+dwIO/8L9oivuIwDXo0SGNUx+pkpsQo9ImcgeLHybqcdbzy4ytM3zQdl3X6TVfOVcdKHZncYTK7H3mUA2+84ZM2M2fNwhOdwNv3LaTgiO+uRS17Z9K4axW+ee0FVkz/1GftyrmLik9gyD+fISImVufpiZQwhb0yyuX2sHjTfga/ugi3R38CUvq0qJbItW2r0rFWKoahNX0ip7AsMAwWbF/AKz+9wqJdi/za3X97TiMjpDzrO3fBnZNz3u2F1qxJ1anTWLcsm69f++X8CzxBv7uakVQxknfuHcvu9Wt92racHUdICAMemkRKlWoKeiI20Dh6GeV0mDSvlsS9PerYXYrIaS3csJ9r/7OUdo99x0uzN5Bf6NYOniKW5f1wF8KuHwF4evnTfg96AHfMGoMRHk65W272SXuFa9aQ88EUal5YnowG5XzSZrGpjy/FVejm8jF3ExYV5dO25ex0vf5WUjIyFfREbKKwV4aZhsHwVhlc27aq3aWI/KFtB/J55IvVNJ74Nff89ye2HvBuMqHQJ2VK8d977m74+j54vCa83BGX6yjD6vpml8y/suHgBubumk/CoEGEVvXNdWP3gw9StHcfHQbXJizSd9vwuwo9fPKvlUTGJ9Bt5BiftStnp+llvanTrqPO0hOxkR59wj3d63BZgzS7yxD5U/lFbt7+fgvtJs1k8CvfM/PXPXgsC49CnwQzy+P9vHUhvD8YnqoD85+B/APgceFc+xVdq3QlNdJ359b9mXGzxuH2uEidMMFnbW678UbCo5y07lvdZ20C7NpwkKVfbCaz8YU07XGFT9uWv1atcTPaXXW13WWIlHkKe4LHsniyf0NaVEu0uxSRMzJ33V6ufn0xHR6fycuzN5CT5z2rS6N9EhSK/46P7IW5T8G/GsNrl8CqT8DjPvm2n48DLAbWHlgipeW58nhrzbtEt29HVKtWPmmz4KefOPTx/8hqVYHKdXx7HVr86SZ2bThI26uupkKtLJ+2LX8sNbMGl91+529/yyJiG23QIgC4PRb5RW6ueG4ea7Nz7S5H5Kw4TIO2NcoxsFllOtVJwXFsMxdt6iIBo/hSbLlh1Wew/D+w/rvfRvb+zPWzyC1XnY4fdCTfle/fOo9Z0H8uIdv3sqHn5eB2//UdzkD1BQsoNCJ4+4GFFBX4pk0A02ly9T9b4i7K4z9jbyH/sI4d8qf41DQG/eNJwiIitU5PpBTQyJ4A3hfL4U6Tt65tTmpsmN3liJwVt8di5q97uOGtpTSZOIN7//czq3cdBjTaJ6Vc8d9n9i/w+ViYlAkfDIV135xZ0AP4cjxRIVH0zOzpvzp/5++LHyUsM5P4vn181ub2W24mIiaEVr19O53T4/Lw8dM/EB4dw6WjxoHeBPKbiNg4+t7zMKEREQp6IqWERvbkJC63hw17j9D73/PJPerbc5pESlqNlGj6NElnQLNKxEeG6tw+KR2OHZlAfg4sfxNWvA3Zq86rSc/tv7Dd9NB9ancsSuay/vkVn1HBE8u6zl3w5PpmRkiFJ54grvul/Pep5Wz/9YBP2izW6OLKtOyVybz33+T7aVN82rZASFg4/R/8J8mVMxT0REoRjezJSZwOk8zkaF4a0oQQh14US2Bbm53Lo1+spsnDMxj+f4v4bOVOjhZ5p4fpfS4pUcV/b66jsOpjeHcAPJYJX91z3kEPwJz9GJViKtGmYpvzbutMjZ01DjMmhqTrr/dZmzvGjcN16DCdhmXhDPXtS5Tl07ewY20OrfsNplLd+j5tu6wzTJPL7pigoCdSCmlkT07L47H434rt3D7lB7tLEfGpMKdJ6+rl6FavPJc2SCMq1KkRP/GP4hG8wiPw8/9g9cfedXiuAr9057pzG8v2r+Kar67xS/un8+rFr9IsqRHrL+lG0fbtPmkzskULKr32GitnbmPuFN8eiG6aMHxSa/AU8PrYW8g7mOPT9suqrjeMot5FnTF0xIJIqaOwJ3/q2W/X8fhXv9pdhohfOEyDC6smcknd8vRsWIEETfWU82V5wDAhbx+s/BBWfwqb5526i6Y/dJsEzW+gz8d9WHNgjf/7AxLCEviuzwyOfPsd20ff5rN20599luhOHZn2+DJ2rj/os3YBEtIi6X93U3auWc2Uh+7COtO1kXJaLfsOotWVg+wuQ0T+gMKe/KW7p63k7e+32F2GiF8ZBjRMj+fieuW5/IIKpMVHHJ/qqfAnf8iyAMsb8A5shp8+gtWfwI7lJb/tvDMc151b+WTDZ9w3/74S6/beFvfSr1Y/Ng26ivxly3zTqNNJjYULOZJv8O5Di3AX+TaQ1e+QTtt+NVg49X3mT3nLp22XJfU7XkzXG261uwwR+RMKe/KXPB6L695cwjersu0uRaTEZKXF0L5mMhfVTKZJRiIhDlOjfuJVPD3T44adP8Dqz7zr8PaWzGjan7rqQ4oyL6LLB13YV7CvRLo0MVk4YD6s38ymPn19FnKj2rUj/YUXWDFjCwumrvdJmyfqcesFVKmbxEf/uI9NP/gopJYh1Ro3o9e4e8Ew9LwoUoop7Mlf8lgWRW4P/V5YwA/bfDudRiQQhDlNmmUk0qZ6OTrXSaF6SgyAwl9ZURzuLA/sXQdrp8OGmbBlIRSWsnNJ4yrjHr2CF398ked/eL7Euu1fqz/3tLiH7X8bz6GPP/ZZu5Vefomo1m34cNISsjcd9lm7AJhw9aOtMB0u3hg3ksP79vq2/SBWvnpN+j/wKA6HU+v0REo5hT05I26Ph9wCN72fn8/6PaXsxY1ICUuMCqVVZhKtq5ejc+1UkmPDNOUzmBSHO4CcrbDmS9g4GzbNgXzfHgfgFzfN52B8Oh2ndKTQU1hi3X7d5yvKFYSwrktXrHwfHe4eGkqNhQs5fMjDew8vwuPy7UuW2ORwBt53Idmb1vH+/ePx+OiA+GCWXKUq/R94lJCwcO28KRIAFPbkjLncHnLyi+jz/Hw278uzuxyRUqNKUiStM8vRqHI8LaolUSkxEkABMBAUXwKL/x/l7vGO3G2c7f04vNO+2s5VZgcY8l/unXcv/1333xLrtklqE/6v62vsfe459j73b5+1G92lM+lPP8OSLzax6JONPmu3WFbrNDpcVYsln05j9tv/5/P2g0lixUoMeGgSYRGRCnoiAUJhT86Ky+1hb24hvf89jx0H/bN9uEigiw130iA9noaV42lWJYEmGYlEhzkBTf20neUBDG+4cxXAjhXe6Zjbl3o/Dvnm+AC7ecauYaMrl17/61Wi/b596dvUi63F+i5dcWX7bp135f+8TkSzC/ngkcXs3er72SXdbqxPtYbJ/Pexiaxf8r3P2w8G8alpDJz4GOHRMQp6IgFEYU/OmsvtYefBAno/P589h4/aXY5IQKiUGEGjSgk0qhxPs4wEapePxenwrnXRCKAfnLhLJngPM9+10hvodv7g/bx3zbHwF4RajoSL/851X13Hwp0LS6zb5IhkZvSezqHPPmfn+Am+azgigprz55Oz38WUvy/G4/H9S5dhj7YiJMzDG+Nu5dCe3T5vP5DFlEtm0MTHiYyLV9ATCTAKe3JOXG4PW/bn0feFBew/UnJrQkSChcM0qJwYSY2UaGqkxlAzJZoGleJIT4gkRCHwzP1+GqbHDbnZkL0Kdq/0BrudP8D+DcEb7P6A664dLMxezk3f3FSi/T7S5hF6ZPZgY9++FPz0s8/ajb2sBxUmTeL7jzey9ItNPmu3WHRCGFc91Jx9Wzfx7r1jcbtcPu8jEEUnJDHgoUnEJJVT0BMJQAp7cs5cbg/r9uTS78UFHMrXRVHEF0wDbwhMjTkeBOtUiCEjMYqwkN9eaJ341B3UYfDES9SJv+eRvbDnV9izGvavh33rYN96yNkM7qKSr7M06vkMNB5Gz2k92XjI92vd/kiIGcKC/vNw/7yazQN9e9h2lXffIbzBBbz/8GL27zzi07YBal6YSqfhWayY/hnfvf6iz9sPNBGxcQx86DHiUlIV9EQClMKenBeXx8OqHYcY+PL35B5V4BPxp9gIJxXjI0iLi6BCfAQV48NJi4sgo1wU6fERJESF4jBPDn6/f4ovFcHw95ed39fkdkFBDhzeBQe3ekfqDmz0hrl967z/LvLRbo/BLDQa1/hNfLRuKg8vfLhEu7667tXc0fQOto0ezeHpX/msXTM6mupz57Evu4APH12K5YfpnF2vqUuNZql88tQjrFk4z+ftB4rwqGj6P/hPEiukK+iJBDCFPTlvbo/Fym05XPXK9xwp1LbVInYxDUiOCaNifATJMeHERYT87sNJUlQYSdGhxEWEEB3uJDLUeUpAPJFleTCMczxHy+MBTyG4Cr2boRTlw9HD3k1QcrPhSDbk7vbugJm7+9jX2VCg8zx9ZtjHHK3cgk4fdOLg0ZL97zqz73fEHipi/cWXYBX6brp/XN8+pE2cyIKp61n+9RaftXuiIX9vSUSUwRvjR5Gza4df+ijNQiMi6Xf/IyRXzlDQEwlwCnviE26PxQ9bcxjyqgKfSKCJCnUQHe4kxGES5jQJcXg/rm6VQe8m6fD+YG9IM0zvKJxhej8sjzfAFeVB0bEw58r/7XuaTmm/5Fp4bl7AM8v+xas/vVqiXbep0IZ/d3qO7CeeZP+rvu0748MPCK1dh/cmLiJnt++PAoqMDWXIw83J2bWdt++6A1dR2VmbHhIWTt97H6Z8tRoKeiJBQGFPfMbtsVhxLPDlKfCJBLy7Lq3N9e0y4dHKGm0LYNbIJeyPSqTzh51xeUp2uv0Hl31AzfAqrOvcGff+/T5r14yPp/qs2ezZnsfUx5aeMjPYF6o1KsfF19Xjp+++5uuX/uX7DkohZ2gYvSfcT8XadRX0RILEOc7NETmVwzRoWCmeN0ZcSGSoLhIiga54V1BKOCCIbxkzHiQpIomLq1xc4n3fMfMOCA0hedQon7bryckhe9I/KV8tjvoXpfu07WIblu/l14W7aNDpYrLadvBLH6VJSHgEfe56iIpZ9RT0RIKIwp74lMM0aFQ5gdevvpCIEF0sRAKZ01TYCwqrP8Gdt4/hdYeXeNdbD2/lu+2ziO93JWE1a/i07Zy33yZ/9a+07J1JbLkIn7Zd7Ns3VpOTfYSu199KYsVKfumjNAiLjKLfvX+nQs3amKZeGooEEz2ixeccpkGTKgm8PqKZRvhEAlios/jsOoW9QOf4/gVqJ9WmcUrjEu97wtwJuFyFpN55p8/b3nLNNRiWRcehtcFPG81+NGkplmVw+dh7cIaF+acTG0XExNL/gUdJqZqpET2RIKSwJ37hMA2aVknk3etaEBcRYnc5InIOfhvZ0xrcgDf7cVxF+QytO7TEuy5wFfDa6v8Q1bIl0e3b+7Rtz7597H16MhVrJlC3TQWftl2sINfF9Jd/Jj61PF2uvcUvfdglMi6eAQ/+k6T0ygp6IkFKYU/8xmEa1K0Qy0c3tSQlJvjeDRUJdiEOA0ujesHB8uBc9SkdKnUgPdo/a9z+zLPLn+VQfg6pd90JTqdP297/6qsUrN9A6741iE7wz7Vm80/7+Wn2Tuq060j9jl390kdJi0kqx8CJjxNfvoKCnkgQU9gTv3I6TDKSoph2S2sqJ0baXY6InAWnw9SoXjD54m94LDeDsgbZ0v0D3z9ESOXKJAzo7/O2t44YgWladByS5fO2i815fw37d+TSacRNJFep6rd+SkJcSioDH3qM2HLJCnoiQU5hT/zO6TBJjQlj2s2tqJUaY3c5InKGnKYBlsJe0Mjfj3PbMvrW7EtUSFSJd//15q/ZdHAjyaNHY8bG+rRt1+7d7Hv+eSrVSaR2yzSftn2ijyYtw+2GnmPuJjTCP5vC+FtCWkUGTnycyIQEBT2RMkBhT0qE02ESFxHCRze1onHleLvLEZEz4HQY2pwl2HwxjjBHGL1r9Lal+zGzxmJGRlLu5pt93va+f/+bo1u20LZ/DSLjQn3ePkBhgYvPn/+J2HLJXHzjbX7pw5/KVarCwImPERETi8Ph2+m0IlI6KexJiXE6TCJCHbxzXQva1ihndzki8hecpqZxBp2dP2DkbGFonaGYRsm/BFibs5b5uxaSOGQwoRkZPm9/yzXX4nAadBhc2+dtF9v+6wF++HYbNVu0pmHX7n7rx9dSqmYy4MFJhEVGaURPpAxR2JMS5TANQhwm/ze8GZfWL293OSLyJ7wjewp7wcb49u+UjypPx0odbel/7MyxuD1uUsb/zedtu7Zu5cBrr5FRvxw1mqX6vP1i8z9az56th+kw/HpSM317fqA/pNepT/8HHiUkPFxBT6SMUdiTEucwDUzT4NlBjRnQLHgPqRUJdE5T0ziD0sopuPNzbDlkHSDXlcu7a98npkMHIps393n7e558ksIdO2g/sBYRMf47+mfqY0txFbq5/I67CIsq+TWQZ6pWy7b0vXsiztBQBT2RMkhhT2xhGgYG8GifBtzYvprd5YjIaXincSrsBSPHkv/jgpQLqJtU15b+Jy2exJGCw5S/524wff9SZOs11xISatB+YC2ft13MVejhk2dXEpmQSLdb7vBbP+ejSfde9LhtPKbDxDQV9ETKIoU9sY1hGABM6JbFhEv8t75CRM6NQyN7weu7h3G5ChhWd5htJTyy5J+E1ahBfG/fbxZTuHEjOe+8Q2bjFDIbJ/u8/WK71h9k6RebyWzSnCY9rvBbP2fNMGg/5FouGnrtsS/1ck+krNKjX0qFGy/K5NHe9TENuysRkWLesKc1e0HJ48K55iu6VulKaqT/1rb9mf+t/x/bD20jecwdmFG+P4d19z/+QWF2NhcNqkVYlP92nlz86SZ2bTxIu6uupkIt/53zd6YcTic9Ro+nSffL7S5FREoBhT0pNfo3q8S/r2pMeIj+LEVKA6dpgLvI7jLEXz4fC1gMrD3QthLGzh6HIzaWpOuv90v7266/gdAIJ+361/RL+8X+98RyCgtc9LzjLiJifHuG4NkIi4yi7z1/p0bzVsdnz4hI2aZX1VJqGIZBlzrl+eDGViTHhNldjkiZp2mcQS53N45dK+lfqz8RTnsOCP9p308s3bOcpBEjCKlYweftH129moMffkjNC8uTUT/J5+0Xc7k8fDx5BeHRMVw6ahzYELRiksox6OHHqVCzNqYf1kGKSGDSs4GUKg7TIKt8DJ/d2oa6Fex7d1REwDQN8GhkL6h9OYGokCh6Zva0rYQxs8bgwSJ57Fi/tL/r/vsp2refDkOyCI3w33TOPVtyWfTJZqrUb8iFl/f1Wz+nU65SFa76x1PEl6+gHTdF5CQKe1LqOB0miVGhfHRTK7rWsWctiYiAwzDArZG9oLZlIdah7QyvOxzvHsklb3/Bfv678RPiunUjolFDv/Sx7cYbCI9y0qZvdb+0X2zZ9M3sWJtDm/5DSK9T3699FUuvU5+BEx8nIiZWQU9ETqGwJ6WS02ES6jR5YUgTbminoxlE7ODQyF6ZYM5+jPSYdNqmt7WthocWPETB0SOk3n23X6ZAFqz8iUOffEJW6wpUykr0efsn+njycgryirjs9glExsX7ta+sth28Z+iF6Qw9ETk9hT0ptUzDwDQM7rw0i8f6NiDEocXmIiXJoQ1ayoalr+M6eti2Q9YBPHh4csXTRNSrR2z37n7pY+eECbhyDtJxWBYhYf4LRh4P/PfJFYRFRtHjtvF+OfbAMEzaXXU1l44cg+lw6Aw9EflDCnsSEPo0Tued61qQEBlidykiZYZpoJG9MsK5/G2alW9GzQT/7lr5Z95d/S7ZubtJGf83jPBwv/Sx7ZZbiIwJoWXvTL+0X2z/jiPMm7qB9Kx6tOzr291OQyMiuWLC/TS9zHs+oXbdFJE/o7AnAcE0DRpViufTW9uSmRxtdzkiZYJpGhga2SsbZtyPy13IkDpDbC1jwrw7cSYlkXT11X5pP3/pUnK/mk799ulUqBnvlz6Krfx2G1tX7adFnwFUuaCxT9qML1+BwY9Mpkr9hgp5InJGFPYkYDgdJqmxYXw8sjVta5SzuxyRoOcw0NELZYWrAOf6mXSv1p2kcP8dUfBXFu9azE/7fibpxhtwpiT7pY/tY8biOnSYzsOycIb692XQJ8/+QP7ho/QYNY7oxPP771qlfkMGPzqZuJRUrc8TkTOmsCcBxekwCQ9x8PrVFzK4RRW7yxEJaqbCXtny2RhMTPrX6m9rGXfMvAMcJsm33+6fDjweto8eTVRCOM0v9/MGYB6Y+vhynGERXHb7necc0hp160mfux4iJCxcQU9EzorCngQch2ngMA0e7lWPB3rW8W4iISI+5wCFvbLk4BYce1YzKGsQoWaobWXsytvFl1u/Ju7yywmvU8cvfeQtWMCRmd9xQcdKlK/m3zNdD2bnM+f9taRVr0mbAUPP6r4Op5OuN4yi4/DrMUxTh6WLyFnTs4YEtKEtM3h9eDPiIrRxi4ivaWSvDJp+F3FhcXSv5p8dMc/UvXPvpch1lNS77/JbH9tGjcZ9JI/Ow+vgCPHvy6Ff5u5k44/7aNazD5lNLjyj+0TGxdPv/kepd1Fnv9YmIsFNYU8CmmkYtKyexJe3teWC9Di7yxEJKqaBDlUvazbMxJO7m6vr+WeDlDNV6CnkhZ9eJrJJE2K6dPFPJy4XO8aOJbZcBM26V/VPHyf44oWVHMkpoNutY4lNTvnT26ZkVGPIo09TPrMGhkbzROQ86BlEAp7TNEmOCeOjm1oxrFWG3eWIBA2N7JVN5tynqRpXlZZpLW2t4+WVL3Mgbx8pd07ACPHP7I0jM2dyZP48GnetTEqVGL/0caKPHluGwxlKzzvuwnQ4T3ubOu06MvDhx4mMi9f6PBE5bwp7EhScponTYfJgz7r8+6rGRIed/iIqImfOUNgrmxY+h6vwCEPrnt36Mn+4Z8F9hJQvT8KQwX7rY+vIW3EXFND56jqYTv+uAT+8r4Dv3vyV5IxqtB8y4qSfOUJC6HL9SLrdcgcOZ4iCnoj4hMKeBJ2L65bn89FtqV3e/+/SigQzAxT2yijnTx/SpmIbqsb6f3rjn5m9bTZrD64jeeRIHAkJ/umkoIBdd95JfEokTS/J8E8fJ1izaDfrl+6hcbee1GjeGoC4lFSu+vuT1OvQFdBB6SLiOwp7EnQcpkGFuHA+HtmGK5um212OSMDSyF4Z9uVduNxFXFXnKrsr4Y7v7oDQUJJH3eq3Pg5Pn07e0iU06ZZBufRov/VT7KtXf+bQvny63Xw7F3S9lCGT/kVSemXttikiPqdnFQlKTodJiMPgsb4X8HjfBoT7eac1kWBkGIbCXllVmItzy3yuqN6LuDB7N7/afHgzs3bMIb5/f0KrV/dbP1uuvwGrsJBOw7MwS+BIn2mPL8N0Oul8zc2E6vw8EfETvQKWoFU8DeaKxul8MrIN1cpF2VyRSGDRNM4y7vNxOE0nfWv0tbsSxs8Zj8tVROqdE/zXSX4+ux64j6SK0TTqWtl//QDRCWFcfF294wFPO26KiL/o2UWCnsM0qFouis9GtaVHgzS7yxEJGBrZK+P2/Iqxbz1D6gzBadq76VW+K583Vr9FdOvWRLVt67d+Dv3vY/JX/ECzy6qSmOafNwir1EtiwL3NSakSo7V5IuJ3CntSJjgdJmFOk2cHNeahy+sS6tCfvsgZ0Tl7ZZox4wGSIpK4uMrFdpfC5OWTOZx/0HvQutN/4XPrddeBy0Wn4Vn4MouZpkHL3pn0GHkBoeEOTF2HRKQE6JlGyoziNRiDm1dh6s2tSE+IsLkikdJNI3vC6s9wH9nL8HrD7a4EgIe+f5jQKlVI6NfPb314cnPJ/vvDJFeO4YLOlXzSZlxKBH0nNKVRF+/0UKME1gSKiIDCnpRBpmlQu3wM029rR5/GFe0uR6QUU9gTcHz/ArUTa9M4pbHdpfDl5i/ZcmgzybeNxozx3/E6OVM+oOCXX2hxeSZxKef3xmBWqzQG3HMhSRWjNG1TREqcwp6USU6HSUSogyf6NeTloU1Iigq1uySR0kcjewIw5wlcRfml4pB1gDtmjcGMiqLcTTf6tZ8t11wLHjedhtU5tlvR2QmLdHLx9fXoODQLR4ipaZsiYgs980iZZR57h7VDrRS+GdOernVSba5IpJRR2BMAy4Nz1Sd0qNSB9Gj7zy5dc2ANC3cvInHoUEIq+2/XTE9ODnsef4y0zDjqtz+737tirQQG3t+cahckAzokXUTso7AnZZ7TYRIbHsJLQ5vy+JUNiAmzd9c5kdJDYU+O+WI8HsvDoKxBdlcCwNhZY/FYHlL/Ns6v/Rx4400K1qyhVZ9MYpLC//L2DqdJ677V6XV7IyJiQjEdCnkiYi+FPRF+27zlikbpzBjTnpaZSTZXJGIv09QGLXKC/P04ty2hb82+RIdE210NhwoPMWXdR8R07kzkhc382teWa67BsCw6Dc3609slVYym393NuKCjd1OXkjiYXUTkryjsiZzAYRqUiw7l3etacP9ldQgP0UNEyqZw57G/fYU9Kfb5OMIcYfSu0dvuSgD4x6J/kHc0l9S77/a+O+En7j172fevp6lYK4E6bSqc8nPDgEZdK3PlXU2JT4nQTpsiUqrolazI7ziOvWgY2jKDL29rxwXpcTZXJFLywovPMXMX2VuIlB67fsQ4sIWhdYbiMBx2VwPApKWPE16rFnG9LvdrP/tefoWjGzbS5soaRCeEHf9+QvlI+k5oSssrMnE4tAmLiJQ+elYS+QMO0yA9PoKpN7fm9i41cerdWilDwo6P7LntLURKFeO7h0mNSqVj5Y52lwLAR2s/Ysfh7aSMHYsRGenXvrZccw2mCR2G1MYwDRp1rUz/ey6kXHq0NmARkVJLYU/kTzgdJg7T4NaO1fl4ZBtqpNi/VkWkJISGaBqnnMbKD3Dn5zC87nC7Kznub3PG44iLo9y11/q1H9fOnex/6QUq10li8MQW3tE8p0bzRKR00zOUyBkwDYOaqdF8Pqot17WthkOjfBLkfhvZ0zROOZljyWs0SG5AvXL17C4FgB/2/MCKvT+SeO01ONPS/NdRSAi43Hg8HqLiwjSaJyIBQWFP5Aw5HSYhTpO7Lq3N56Pa0rBSvN0lifhNmPPYmiyN7Mnvffd3XK4ChtYpHYesA9wx8w4swyBlzBi/tB9ery7Vpn5EuVtHYpomDqdePolIYNCzlchZMgyDzJQopt7ciod71SM2QufySfDRmj35Qx4XzjXT6ZrRldTIVLurAWBvwV4+2fQZcT26E96ggc/aNcLCSB4zhowpUwitWhXDj7t+ioj4g561RM6B0zQxDYOBF1Zm1tgOXN7w1O24RQJZqI5ekD/z+TiwLAbVLh2HrAM8uPBBCo7mUf6eu33SXlSb1lT74nOSrhmBYZoYTr2xJyKBR2FP5Dw4TIO4iBCeHtCId69rQbVyUXaXJOITx8Oejl6Q08ndjWPnj/Sv1Y8IZ4Td1QDg8rh45sdniWjQgNhLLz3ndpzJyVR46kkqv/IKIampGs0TkYCmZzCR82Qe26ylWUYC029vx+2da/w2BU4kQIU5NLInf+GL8USGRHF5pn/PuDsbb/7yJnuOZJMy/m8YYWF/fYcTmSYJgwaROf1LYrt0AcBwlI7zBEVEzpVekYr4iNNhEuIwubVjDWbc0Z421cvZXZLIOfvt6AWt2ZM/sG0R1qHtDKs7DIPSszPlXXPvxpmcTOLwYWd8n/D69an60YeUv+9ejIgITdkUkaChsCfiY6ZpUCE+nLeubc4zAxuRHH2W7y6LlAIhGtmTM2DOmkR6TDpt09vaXcpxC3ctZNX+1ZS78UYc5f78TTdHfDzlH3qQjPffI6xGDQAdqSAiQUVhT8QPHMfWeFxarzwzx13EkBZV0NF8EkhCHTpnT87Asv/gPnqoVB2yDt6jGAhxknLb6NPfwDCI79uXzK+mE9+njzZgEZGgpbAn4kdOh0lkqIOJverxycg2XJAeZ3dJImckLEQje3JmHMvfpln5ZtRMqGl3KcdtP7Kdr7d9S1zv3oTVrn3SzyIaNSLjow9Je3giZkyM1uWJSFBT2BPxs+IpQbXKx/C/kW3418BGpCeUjt3rRP7Ib9M4tWZP/sKM+3G5CxlSZ4jdlZzknjn3UOQqJPXuuwBwpqVR4cknyHj3HcJreoOppmyKSLBT2BMpIc5jL5671SvPd2Mv4u7uWcRFhNhclcjpac2enDHXUZzrv6N7te4khSfZXc1xBZ4CXv75VaKaNaPC4495d9ns2hVAUzZFpMxQ2BMpYcW7do5oXZW54ztwTZuqv62PEiklQhzHRjx0zp6cic/GYmLSv1Z/uys5zjRMduXtwuN2EXvppZihoQp5IlLm6BWmiE0cpkF0mJO7u2cxc9xF9GiQZndJIseFOo+tY9LInpyJg1twZK9iUNYgQs1Qu6uhRVoLPrzsQya2ngimqYPRRaTM0rOfiI0Mw8A0DMrHhvPsoMZ8MrINzTIS7C5LhJDi7WO1Zk/O1PS7iQ2NpXu17raVUD2+Oi90foGXu75MtbhqgHeET0SkrNIzoEgpYB57YZ2VFsMHN7bilaFNqVYuyuaqpCzTmj05axtnYuXu5up6V5d41+UiynF/y/v5qOdHNE9rDoDD1C6bIiIKeyKlSPEmLhfVSubrO9oz8fK6JEXZPyVKyh5n8Zo9nbMnZ8Gc9zRV46rSMq1lifQXGxrLrY1u5YveX9Crei9Mw8Rpal2eiEgxhT2RUsjpMHGYBgObV2bO+A7c0qE64SF6uErJCdXInpyLhf/GVXiEYXWH+bWbqJAobmxwI1/1/Ypr6l9DuDNcIU9E5DT06lGkFHOaJpGhTsZ0qcmCOztxQ7tqRIZqapL4n1Pn7Mk5cq78gNYVW1M1rqrP245wRnBNvWv4uu/X3NTwJqJConAYek4UEfkjCnsiAcA0DeIjQhh/SW0W3tmJWzpUJyZM72KL/4SYBpaCnpyL6XfjchcxOGuwz5oMc4QxpM4Qvu77NaMajyImNEYbr4iInAE9U4oECMMwME2D2IgQ7jg20ndb5xrERij0ie85HIamcMq5KczFuXkevar3Ii4s7ryaCnOEcVXWVUzvM52xTccSGxqrkCcichb0jCkSgBymQXS4k1s71mDhnZ0Y27UWCZEhdpclQSTUYYKlkT05R5+NxWk6ubLmled09whnBEPrDOXrvl/zt2Z/IzE8EdMwMQzDx4WKiAQ3w7Isy+4iROT8uD0WRW4Pr8/fxCtzNrA3t9DukiTAvTqsKR2rRWI8km53KRKgrFsWcyA6iU4fdsZ1hqPEkc5IBtQewIh6I4gJjcHAUMATETkPGtkTCQIO0yA8xMF1basxb0JH7u2RRUpMmN1lSQBzOgxtziLnxZhxH4kRSVyScclf3jY6JJrr6l/HjCtnMLrx6OPTNRX0RETOj0b2RIKQy+PBsuDdRVt4YeZ6dhwssLskCTBvX9ucVmlgPJZpdykSwNzj1rPu6D76ftL3tD9PjUxlUO1B9K/dnwhnhNbjiYj4mHZ2EAlCTtP7gmlQ88oMurAyHy3bxmtzN/Hr7sM2VyaBwjuypwPV5fw4Fj5PrU730iS1CUt3Lz3+/ZoJNRledziXVr3UeztTxyeIiPiDwp5IECsOfX0ap9O/WWUWbtjHq3M38s2q3Xg0pi9/wmlqGqf4wJzHcbUby9A6Q1m6eymtK7Tm6npX0zytOS6PSyFPRMTPFPZEyoDiA7KbZiTQoloSO3LyeXXuRj5YspVDBdpeX07lMHX0gviG85ePuajBlXzc62OqxlU9vlmL09RLEBERf9PkeJEypHikLy0unLsvzWLR3Z2ZeHk9MpOjbK5MShuHobAn5ymmPHS4G2p2xe1xUyW2CqCQJyJSkvSMK1IGGYaBYUC46WDghZUY0rIKc9fu4ZW5G5m1Zg/atkmcDlNhT85NejNofhPUvdz7telEp4CKiNhDYU+kjCue4tmiWhJtaiSzdX8er87dyIdLt5F7VC/2yypN45Sz4giFur2gxS1QoSG4i0AjeCIittPRCyJyEs+xp4SCIjfvL97K6/M3sXlfns1VSUmbcUd7Ml1rMV66yO5SpDRLqAoNB0GzayAyybupjzZdEREpNfS2m4icxDx2iHFkqJMhLaowrFUGC9bv4/3FW5n+8y6Oujw2VyglwWEa4NbInpxGaBTU6QWNh0LlFt4R4OJRPAU9EZFSRWFPRP5Q8RTP5tUSaV29HEeOuvjfiu18uHQby7bk2Fuc+JWmccopqrSChoOhXm9whoN17I0fTdcUESm19AwtIn+peBfPqDAn/ZpWYlDzKmzed4T3F29l6rLt7DpUYHOF4mve3Th1qHqZF5cOFwyExsMgvpJ3LZ7j2HYrhkbxRERKO63ZE5FzYlkWHgsMA+av28uUJds0zTOILLizI+X3zMN4q4/dpUhJc4ZDVg9oNBSqtgXLAsP0PthFRCSgaGRPRM6JYRg4jr32a5Hp3clT0zyDh8PQmr0yJ72pd5pmg37edXke17GQZ3dhIiJyrhT2ROS8aZpn8HEYYGgaZ/CLToULBng3W0mqfvI0Ta3FExEJeHomFxGfKt7UpXJiJGO61mLsxbVYsmk/n63cxfSfdin4BQjTwLuNvgSfmPJQuwdk9YSMNt7vFU/RdOj4cxGRYKKwJyJ+ceI0zyZVEmmakciDPeuycvtBPvtxB1/+tItNOr+v1PKGPY3sBY3EapB1mffIhIqNvTtpWpaOShARCXIKeyLidw7zt0U/dSvEUictlgndsliXnesNfj/vYtXOwzZWKL/nDXtasxfQyjfwbrRSpxck1/KO1BaP4GktnohImaCwJyIlyjSM4y8yM5OjuKVjdUZ3rsn2A/l8emzEb8W2HLRPsL0cCnuBxzChUvNjAe8KiKt4bJOVY6N3GsUTESlzFPZExDaGYeA8NtJQMSGCa9pU5Yb2mezNPcpnP+7ky593sWjjftweJb+SZmjNXmBwhELVdt4pmlk9ITJRm6yIiMhxugqISKlRvLlLuegwBjWvzLBWGRwqKOLrn3czZ+1e5q3by57cozZXWTaY4A0NUvqERkP1zt6AV6ub95iEEwOeNlkREZFjFPZEpFQKORb8YsND6NmwAn2apAOwNvsws37dw9y1e/l+437yizT65A+GpnGWHs4w7/TMqu2gWkeo0NA7JVMBT0RE/oLCnoiUesXBD6BGSgwZSVFc27YaLreHZVtymL3WG/5Wbj+oKZ8+orBnI9MBFRpD1fZQ7SKo3Nw7XdNd5J2WqWMSRETkDCnsiUjAKQ5/TodJ04wEmlRJYGzXWhw56mLuur3MWbOHuev26miH82BgaM1eSTEMSKkL1dpD1Yu8Z9+FRh7bXMX0foDCnYiInDWFPREJaCfu7hkV5qRTVgpd6qRiGga7DhYwc002c9fuZfGm/ew+pPV+Z8rQOXv+lZTpDXZV20FmBwiP+y1cF++aqc1VRETkPOlKIiJBxWn+NuWzfFw4fRqnM6BZZQD2HD7Kkk37Wb41h+VbDrBy+0EKijx2lVqqeUf2NI3TJwwDkmpAejOo2hYyO0F0ivdgc48HHMcuxToaQUREfExhT0SC2onr/ZJjwuhatzxd65bHYRq4PRbrsnNZvGk/K7bmsHxLDhv25uqMP/COlirsnZuY8lCxifcjvZn3c2iU92cnbqpimHDC36eIiIivKeyJSJniMI2T/l2rfAzVkqO4qnllDMPgyFEXy7fmsGzzAZZvyWHF1gMcyCt70xkNQ2v2zkhEAqRdAGkNvaGucnOITvX+zF3kHa0zTgh0WncnIiIlSGFPRMq8E0f/osKctMpMokXVxOPn/m3PyWfJpv38uuswa7NzWbP7MFv35xHcG38aOmfv92IreINd+QbezxWbeEfx4LdR0BPX2SnYiYiIzRT2RER+xzQMTMdvI4AV4yNIrZ9G9wZpx9cEFro8bNx7hFU7D7Fm92HWZeeyNjuXzfuOBEcILMvTOGMrQGImJFX3bqSSWt97tl1EvPfnbpd3Hd6Ja+y0mYqIiJRCujqJiJwB5+/WVoU6TWqVjyEzOQpIO/7zQpeHzfuO8MvOQ6zNzmXt7lzW7j7M5v15AXYGoBncYS8y6bcwl1TdG+5SakN8BoSEe29jWd4dSU3n76Zi6tIpIiKBQVcsEZHzcLoQWCM1hqrJUWD99vMit4ct+/LYvD+P7Qfy2ZGTz46D3s87Dxaw62ABrtIUBoNhzV5Y7LEwl/nbSF1yLe+/w6J/u527EAzHqbthGob3MHMREZEApbAnIuIHJx4BAd51gZkp0VRLjsLlsTA4OSh6LIv9RwrZkZPPln157DhY4A2Ex0NhAfuPFJZI7aZZvEFLKV2zFxbrPbogOsW7GUpUsvdz8dexFSC2IkQm/nYfd9GxqZenuewp0ImISJBS2BMRKUGGYRBywnrAYqZhUC46jHLRYdStEIfbY+E0DcwTdg8tdHnYfaiAA3mF7D9SSE5eEQfzf/s4lH/y18XfO1J4diN0ocVBtSSmcRomhER4P4pDXFTK78JcijfARadCVNKp4czjBssNHAtzxqn/fbVZioiIlEUKeyIipYzDNE46IqJYqNOkUmIklRIjAXB7LDyWBRaYf3AfAJfHw5Gj7uNhcP+RQnKPuihyeyhyeyh0eSh0WxS6vF8fb6ZGV2/QsjzeD6zf/m0d+3dxWHOGQ0ikd71bSOQJX0dAaLT336HHfu48dntn2B+HMMtzLGz+SYArZjoAHUguIiLye4Zl6fhgEZGyxmNZWJbFsax40kHyhgEOqwizeFOS40HLOPlGxYrDH8caM4xjH+bJG5uIiIhIiVLYExERERERCUJ6y1VERERERCQIKeyJiIiIiIgEIYU9ERERERGRIKSwJyIiIiIiEoQU9kRERERERIKQwp6IiIiIiEgQUtgTEREREREJQgp7IiIiIiIiQUhhT0REREREJAgp7ImIiIiIiAQhhT0REREREZEgpLAnIiIiIiIShBT2REREREREgpDCnoiIiIiISBBS2BMREREREQlCCnsiIiIiIiJBSGFPREREREQkCCnsiYiIiIiIBCGFPRERERERkSCksCciIiIiIhKEFPZERERERESCkMKeiIiIiIhIEFLYExERERERCUIKeyIiIiIiIkFIYU9ERERERCQIKeyJiIiIiIgEIYU9ERERERGRIKSwJyIiIiIiEoQU9kRERERERIKQwp6IiIiIiEgQUtgTEREREREJQgp7IiIiIiIiQUhhT0REREREJAgp7ImIiIiIiAQhhT0REREREZEgpLAnIiIiIiIShBT2REREREREgpDCnoiIiIiISBBS2BMREREREQlCCnsiIiIiIiJBSGFPREREREQkCP0/kxWI/37cdxEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "grouped_data = df[\n",
    "    (df['src_ip'] == '10.0.0.4') & \n",
    "    (df['dest_ip'].str.contains('162.125')) & \n",
    "    (df['@sourcetype'] == 'stream:tcp')\n",
    "    ].groupby('bytes').size()\n",
    "\n",
    "grouped_data = grouped_data.sort_values(ascending=False)\n",
    "top_5 = grouped_data[:5]\n",
    "others = pd.Series(grouped_data[5:].sum(), index=['Other'])\n",
    "plot_data = pd.concat([top_5, others])\n",
    "\n",
    "plt.figure(figsize=[10, 10])\n",
    "wedges, texts, autotexts = plt.pie(plot_data, \\\n",
    "    autopct='%1.1f%%', startangle=90, \\\n",
    "        wedgeprops=dict(width=0.3, edgecolor='w'))\n",
    "plt.setp(autotexts, color='white')\n",
    "plt.setp(autotexts[:5], color='black')\n",
    "plt.axis('equal')\n",
    "\n",
    "center_circle = plt.Circle((0, 0), 0.70, fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(center_circle)\n",
    "\n",
    "plt.legend(wedges, plot_data.index, title='Categories', \\\n",
    "    loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.31 Jupyter notebook code – Show the distribution field bytes for connections between 10.0.0.18 and IP addresses that contain 162.125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EfPznMZRUaJITERGZj9B17F+1FIc3rcOAsTejz6gxXMSFiJwOyx5RE9F0AYuqYNOxbLz0+yEkZRfLjkREZHoVpSXYMH8O9q1cisF33IXomEuhaxpLHxE5BZY9oiag6wIZ+WV44bcDWB2XKTsOEZHTKcg6hcUzp2P30k644q5JaB7VFkIIns9HRKbGskdkRzZNhyYEPlxzDJ+vT0S5TZcdiYjIqaUdicO3zz6GbkOHY8idd8PF3Z2jfERkWix7RHZwdsrmqrhTeHVxHFLzuCk6EZHDEAKxa5YjfvtmXHrbRHS/cgSErrP0EZHpsOwRNTIhBJJzivH8rwew6ViO7DhERHQeZUWFWPX5RziwZgWG3T8FIa3bcGonEZkKyx5RI7FpOio0He+uOIq5m4/DpgvZkYiIqBYyEo7i22ceQ49hI3HZHf+C1cWVo3xEZAose0QNpOk6LKqK3/el4fWlh5FVWC47EhER1ZEQOvauWIL47Ztx+cT70PGSwdB1jfvzEZGhsewRNYCmC2QWluPJH/ZxyiYRkQkU553GkvffwoG1qzDsvsnwCQqGqqqyYxER1QtfvYjqQTszRXPetmRc9e46Fj0iIpNJ3rcbc6Y+iF2Lf4bQdeiaJjsSEVGdcWSPqI40XSA9vxRPfL8P25JyZcchIiI7sVVWYP28r3B06yaMnPI4AkLDoHCUj4gMhK9YRLVk03XoQmDO5iQMm7GeRY+IyElkJBzF109NwbZfvofOUT4iMhCO7BHVgqYLpJ4uxePf78Ou5NOy4xARURPTbDZs+u5bxG/bjJFTnkBQeCS3aCAih8eRPaILsGk6dF3g8w2JGP7eehY9IiInl3k8Ed88/Sg2fz8PuqZxlI+IHBpH9ojOQ9cFTuSWYOp3e7HvZL7sOERE5CB0zYatixbi2I4tGDnlCQS3iuIoHxE5JI7sEf2NTdOh6QIfrz2Gq2duYNEjIqJzyk5JxrfPPoaNC+ZCs9k4ykdEDocje0R/oQuBxOxiTP1uLw6mFciOQ0REDk7oOrb/+iOS9u7CtY89A//QFtyXj4gcBl+NiFC1AIsuBD75MwGjPtjAokdERHWSlZyEb/79MPatWAIA0HVdciIiIo7sEcGm6cgrrcTDC/ZgSwI3RyciovqxVVZgzVefImnPLoyc8jjcPL2gWiyyYxGRE+PIHjm9jceyMfy99Sx6RETUKJL27sRXjz+I4/t2AwCEEJITEZGzYtkjp2TTdNg0Ha8uPoS75uxAbnGF7EhERGQipQX5+PnNl7Hqi4+h2SqhazbZkYjICbHskdPRdIH0/DKM+WQTvtyYBL7hSkRE9rJv5VJ88+9HkHMyBYLn8RFRE2PZI6dxdhrNb/vScPXM9TiQykVYiIjI/nLTTuLbZ6dix++LAHDxFiJqOlyghZyCTdNh0wX+83MsFu1OlR2HiIicjK7ZsGH+HJyMO4BRjzwFFzc3qBYehhGRfXFkj0xP1wWOZRbhmvc3sOgREZFUSXt2Yu5TU5CZnMRpnURkdyx7ZFqaXjVtc+6W47juo01IzC6WnIiIiAgozM7Cgueewp7lVXvysfQRkb1w/gCZkk3TUVyh4fHv92J1XKbsOERERDXomg1/zvkUqYcP4uqHpsJitXJPPiJqdBzZI9PRdYEDqfkY8d56Fj0iInJoR7duxDdPP4zT6alcuIWIGh3LHpnO9ztTcPOnW5FRUCY7ChER0UWdTk/Dt89OxaF1awBwE3Yiajwse2QKmq5D0wWe++UAnlkUiwqN744SEZFx2CrKsXz2TCybNRO6zQZd02RHIiITYNkjw7NpOgrLbLj98634dmuy7DhERET1dnDtKsx//kmUFOSx8BFRg7HskaFpZ7ZVGPXBRmxLypUdh4iIqMEykxLwzdOPIiMhnit1ElGDsOyRoS09kI4xn2xCal6p7ChERESNpiQ/D9+//AwOrF0lOwoRGRjLHhmOpgvoQmD60jhMmb8HZZV815OIiMxHs9mw4tMPsPp/s6HrOlfrJKI64z57ZCg2TUe5Tcfkebux9miW7DhERER2t3f5YuScPIHrn/gvrO5usFh4+EZEtcORPTIMTRdIOV2Kaz/cyKJHREROJeXgfnzzzCPIz0jnwi1EVGsse2QIQgisPZKJ0R9uRFJ2sew4RERETS4/8xS+/c/jSNqzk3vxEVGtsOyRQ9PP/DL7cM0x3Pv1ThSV2yQnIiIikqeyrBS/vPMatv38HQBuwE5EF8ZJ3+Swzi7E8uh3e/D7/nTZcYiIiByDENj03bfIP3UKwx54GEIIqCrfvyeif2LZI4d0diGWe+fuxJbEHNlxiIiIHM6BtStReDoH1z/xX8BqhWqxyI5ERA6GbwORw7FpOvJKKzFu1mYWPSIiogtI3rcbC55/EqWFhVy4hYj+gWWPHIpN05FyuhTXf7QJhzMKZcchIiJyeFnJSZj3n8dwOiONhY+IamDZI4eh6QKxqfkY8/EmpOaVyo5DRERkGIU52Vjw3JNIPXwQgpuvE9EZLHvkEHQhsObwKdz62Vbkl1bKjkNERGQ45SXF+HHaCzi0ca3sKETkIFj2yCHM33YCD3yzC+U2vhtJRERUX7pmw7KPZ2DLjwsAcGsGImfHskfSnP0F9M6KI3julwPQ+fuIiIioUWz+YR5WfPoBIASE4BupRM6KWy+QFLouIAA889M+/LDzpOw4REREphO7ZgXKiotx7aP/hlB07sVH5IT4U09NTtN1VGhVe+ix6BEREdlP/LZNWPTmy9A1G1fqJHJCLHvUpGyajqIyDbd8ugV/HsmUHYeIiMj0kvftxo+vPgdbZQULH5GTYdmjJmPTdGQWlmPMJ5uw72S+7DhEREROI/XIIXz34tMoLy1h4SNyIix71CRsmo60/DLc8MlmJGUXy45DRETkdDKPJ2LBc0+ipCCfhY/ISbDskd3ZNB0nckswbtZmZBSUyY5DRETktE6np2L+c0+gIDuLhY/ICbDskV3ZNB1J2cW4afYWZBWWy45DRETk9Aqzs7Dg+SeRm3aShY/I5Fj2yG5suo6jp4pw06dbkFNcITsOERERnVGSn4eFL/4bmUkJLHxEJsayR3Zh03UcTCvArZ9tQV5Jpew4RERE9DflxcX44bX/svARmRjLHjU6TRfYl5KP2z/bioIym+w4REREdB4VpaX44bXnkHk8kYWPyIRY9qhRabrA3pQ8jP9yG4or+EuDiIjI0VWUluCHV//LwkdkQix71Gg0XcfelDxM+HIbSlj0iIiIDKO68CWz8BGZiVV2ADKHs1M3J3BEj8iQPF0t8HK1wsvNAi83KzxcLLCoClRFgaIAFlVBpxa+AICw9h3h7u0NoevQdR3izJ+zf7eVl6OsuAhlRUXQNU7lJjKKitIS/Pjqc7jp+WkIbhUF1WKRHYmIGkgRQgjZIcjYqooep24SOQI/Dxc093VDiI87QnzdEOLjhua+7gjycoWXuxX+7i7w9XCBt5sVHq4WeLhY4GJVoSpKrW5f6DoUtfaTQmwVFagsK0VpcTFKC/NRkp+P0oJ8lBcXoayoEKVFhWf+XnSmIBairKgQFaWl9f0WEFEDuXl6sfARmQTLHjWITdcRezIfd37Bokdkb56uFkQ180JUMy9EBnqiuW9VoYsM9ERzH3f4ebrAxVKziP39JV6pZamzp9pkqqyoQEHWKeScPIG8jPSqP6eq/hTl5EAIvaniEjklN08v3PTC6wiObM3CR2RgLHtUbzZdx4GT+bjzy+0oKudULaLG4GJREBnoeabUeSOqmRc6NPdGuxBv+Hu6Vl/vry/djlDg7OVcj1PXNBTn5SL7ZApOp56sKoFnymBB1iloNr4eETUGNy8v3PzCG2gW0YqFj8igWPaoXmyajoSsYtw0ezO3VyCqp/AAD3Rt6YduLf3QLcwX0aG+CPZxg6pWlZqzL89mLnMN9ffvkRACJfl5yDmZglOJ8chIiEdGwlEUZGXKjElkWG5eXrjtlbcR0CIMqoVLPRAZDcse1ZlN03GqoBxjPt6ErKJy2XGIDCEy0BNdW/qiW0s/9IzwR/dwf3i5VR04sdQ1vr9/T8tLSpB+9DDS4g8j49hRZCQcRWlhgcyIRIbh5R+A2157B96BQbCw8BEZCsse1YlN11FQasOYjzfhRG6J7DhEDinY2w39owLRLdwPvSL80S3cD56uLHay/f17X1yQj9S4g0iPP4yMhHicSjyGyjIuDEN0Lr7BzXHHtHfh7u3DKZ1EBsKyR7Wm6TrKKnXcOHsz4tILZcchchjBPm4YGBWIgW2CMKRDMMIDPQGw2BnBX/+NhBDIzzqFk4dikZEQj7QjcchKTpKckMhxBIVH4rZX3oaLuzsLH5FBsOxRrehCwKYJ3PHFVuw4flp2HCKpQnzcMLBNEAZEBeLy6GC0DPj/csdiZ3x/LYClhQVI2LUNx/ftQfL+PSgr4htd5NxC23XAzS++AYvVBWodtmEhIjlY9uiihBDQBXDf1zux5jAXOSDn4+1mxWXtm+Gy9s0wpAPLnbM5++8shED2ieOI374Fx/ftQsaxeG4BQU4pslsP3PDMy1BVtU77bhJR02PZo1p5/Pu9WLQ7VXYMoiYTHuCBKzs1x9VdmqN/60BYLCrLHQH4//JXWV6OxD07kLR7B47v243iPM56IOfRrn8Mrpv6LKAofF0kcmAse3RRry4+hC838rwVMjdFAXqG++PKTs1xTbdQtAn25jl3dFF/fY7kpp7EsR1bkLRvN9KOxEHXuC0NmVvXy4dhxIOPyo5BRBfAskcX9Mmfx/DW8iOyYxDZhaerBZe1b3ZmBC8Uvh4uLHhUb3997lRWVCB5327EbfwTibt2wFZZITkdkX30GTUGl0+4V3YMIjoPlj06J10IfL8jBc8sipUdhahRuVgUDOkQjBt6tcSwLqFw4fRMspOzzytbRQUOb9mAuA1rkHIgluf5kekMvuMu9B19A19HiRwQyx79g6YLrI47hQfn7Yam8+lBxqcoQL/WgRjTMwzX92wJLzcrCx41qbPPt7KiQhxYuxpxG/9EZlKC7FhEjUNRcN3UZ9G230Cu0EnkYFj2qAabrmPn8dOY+L/tKLfx3WcytujmPhjTKww39o1AsLcbCx45hLPPw/ysU4hdswKHN65FfuYp2bGIGsTq4opbXpqOkKi23IOPyIGw7FE1m6bjWGYRbpy9BUXlXFiAjCnMzx3X9QzDzX0i0CbEmwWPHNrZ5+epxGOI/XMFjm7ZiNLCAtmxiOrFw8cXd74xE96BQSx8RA6CZY8AAJquI7/UhlEfbEB6fpnsOER1YlEVXNExBP+6pBUGtQvmIitkOGefs0IIJMfuxaG1q3Bs5zbYKsolJyOqm4AWYbhj2ntwcXdn4SNyACx7BCEEbJrATZ9uwd6UPNlxiGot2NsNt/SPwL8uaY1mnKZJJnH2eVxRVob9K//AnuWLUZCVKTsWUa21jO6Mm154HapFhaLwHD4imVj2CADwyII9+G1fmuwYRLUS0yYIE2JaYXiXUKgKR/DIvIQQgBA4tnMbdv3xC1LjDsqORFQr0ZcMxrWP/lt2DCKnx7Ln5IQQ+PjPBLyzgnvpkWPzdbdiXJ9w3D0oChGBnhzFI6dy9vmec/IEtv/6I45sXg/NxnOrybH1v/5GXHb7v2THIHJqLHtO7OwWCw98uwt8FpCj6tDcG/dc2gZje4fB5cyS3ix55KzOlr7ykmLsWvIr9q38AyX5ebJjEZ3XsPunoNvQ4VC4JQORFCx7Tsqm6UjIKsbYTzahpEKTHYfoH/q2CsCUK9rh8ugQjuIRnYMQAkIIHNm8HjsX/8x9+8ghqRYLbnzuNbSM7swFW4gkYNlzQpoukF9aiWs/2IA0rrxJDkRRgKHRIXj0ivboEenPkkdUC2d/TtLjj2DHbz/h2M6tEDr3SSXH4eHji/Fvvg8v/0AWPqImxrLnZIQQsOkCt3y6BbtP5MmOQwQAsKoKru0ehkevao+oZl4seUT1cPbnpvh0LnYu+QX7Vi5FZVmp7FhEAIDgVlG4/bV3YbFaOaWTqAmx7DmhxxbuxS97U2XHIIK7i4pb+kViyuXtEOzLrROIGsPZX+sVpSXY+tNC7F2+BLbKCsmpiIAOAwdh9NRnZccgciose05ECIFZaxPw1nKuvElyebtZcdeg1rh/cBt4u1kBcNEVosZ29td7WVEhNn3/LWJXr4CucQVPkmvQLeMxYOzNfM0naiIse05C0wX+PJyJ+77ZyZU3SRo3q4rxMa3w2JUd4OVm4S97oiZw9td8SUEeNsybg0Mb/uQ5fSSPomDsv19A6x69ef4eURNg2XMCNk1HYnYxxnzMlTdJDouq4MY+4fj3iGgEermy5BFJlJ+VifXf/g9Ht20C3/0jGVw9PHHn9JnwC27OwkdkZyx7JqfpAoVllRj1wUak5vFEfWpaigKM7BqK/1zTCeEB3AidyBGc/TnMTU3Bum//h8TdO2RHIicU0KIl7pw+E1ZXN6hcsIXIblj2TE4XAnd+sQ2bE3JkRyEnM7h9M/znmk7o2MKXJY/IAZ39ucxIjMf6b79CysH9siORk4nq2Rdjn3mRvx+I7Ihlz8SEEHh/dTxmroqXHYWcSK8If/x3VEf0bR3EkkdkAGd/Tk8eisX6+XOQHs9FvKjpDBh7MwbdMp6/K4jshGXPpGyajp3Jp3H751uh81+YmkB4gAdeGNUZw7uGsuQRGdDZn9vE3Tuw7tsvkZt6UnYkcgaKgnHPvozIrj14/h6RHbDsmZCmC+SXVmLEe+uRVVQuOw6ZnJtVxaQhbTHlinawqgpLHpHBCSEgdB27lvyCLT8t5MbsZHcePr6Y8PZH8PT1Y+EjamQseyakC4E7vtiGLTxPj+xsWOfmeG1MV4T4uLHkEZmMEAJlRYVYM+czHN64VnYcMrmw6E645aU3uVgLUSNj2TMZIQTeW3UUH6w+JjsKmVibZl54dUwXDGoXzCmbRCZ29uc79WgcVn/xCbKSk2RHIhPrO/oGDLnzbtkxiEyFZc9EbJqO7Um5uPPLbTxPj+zCy9WCh69oj/sui4LKKZtETkMIAQiBvSuWYNN336K8pFh2JDIjRcGYp55HVM8+nM5J1EhY9kxC03XklVRixMz1yC6qkB2HTOi6HmF4aXQXBHi5sOQROSkhBMqLi7Dqy1k4snm97DhkQu5e3pjw9kfw8g9g4SNqBCx7JqHrArd/sRVbE3NlRyGTaR3kiXdu6oG+rQM5ZZOIql8HkmP3YuVnHyI/85TsSGQyoe064LZX3mbZI2oELHsmIITAuyuP4qM1PE+PGo+qAHdfGoWnr+7IVTaJ6B+EENA1DZu//xY7F/8MXdNkRyIT6X3NdRg68X7ZMYgMj2XP4Gyajq2JORj/v+3gvyQ1lrbB3nj/1p7oEubLkkdEFySEwOn0NCz75D2kxx+WHYdM5Lon/ou2ffpzhI+oAVj2DEzTBU4XV2DEzPXIKeZ5etRwFlXB/Ze1wRPDO8DC0TwiqqWzhxJ7VyzB+m+/gq2Ce7xSw7l5eeFf787i/ntEDcCyZ2C6LnDr51uxPYnn6VHDRTf3wfu39kR0qA9LHhHVixAC+ZkZ+P296chMSpAdh0wgokt33PzC67JjEBkWy55B8Tw9aixWVcFDQ9vi0SvaczsFImowIQSErmPjwq+x8/efIYQuOxIZ3JA770afUWOgcMN1ojpj2TMgm6YjLr0AYz7ZDI0b6lEDdAnzxcxbeqJdiDdLHhE1KiEE0o4cxpIP3kRhTrbsOGRgFqsV49/8AAEtWnI6J1EdsewZUIVNx8j31yMhi5vaUv0oCnDvpW3wzMiOUBWw6BGRXQghYKuowPJZM3FkywbZccjAmkW0wp3T34fFapUdhchQWPYM6JXfD+J/m47LjkEGFeTlig9u7YlB7YNlRyEiJ3Jow1qs/vJjVJSWyo5CBsXtGIjqjmXPQGyajt0n8nDLZ1u4zQLVy6B2Qfj49t7w83DhaB4RNSkhBIrzTuP3Ga8j7Si3aKB6UBTc9Nw0hHfqDNXCET6i2mDZMwghBMptOoa9tw4puXxXlOrGqip4fFgHTLq8LRRw2iYRySGEAITAlp8WYuuihRA6F2+huvEODMK/3v0Eru4eXLCFqBZY9gzkP4tiMX/7CdkxyGDCAzww647e6NrSjyWPiByCEAKZSQn4feZ05J/KkB2HDKbDwEEYPfVZ2TGIDIFviRiATdOxMT6LRY/q7JpuoVg5dTCLHhE5FEVREBLVFv965xN0GXKl7DhkMEe3bsLBdWuga5rsKEQOjyN7Dk4XAqUVGq58dx0yCspkxyGDcHdR8eLoLritfySEECx6ROSQzr4+xW1cixWffghbRbnsSGQQbp5euHvmp3D38YXK6ZxE58WfDgenKgqe//UAix7VWniABxY/fClu7RcBgOfnEZHjOvv61HHQENw+7V14BwZJTkRGUV5SjBWffciiR3QR/AlxYDZNx6pDp7Bod6rsKGQQMW2DsOzRwWgbzE3Sicg4FEVBs4hWmPjOxwjr0FF2HDKIhJ3bcGTLBk7nJLoAlj0HpQuB4goNzyzaLzsKGcRdg1pj3j0D4OVmYdEjIsNRFAVunl645aU30fXyYbLjkEGs/nIWKspKubIr0Xmw7DkoVVHwzE/7kV1UITsKOTg3q4p3buqOF0d3gaoqLHpEZFiKokC1WDDiwUcxdOL9XFqfLqq0sACrv5zF5wrRefAnwwHZdB2/70vD0gNcjpouLMTHDYsevATjeofLjkJE1Kh6jRyNm56fBncvb9lRyMEd3rQOibt3cDon0Tmw7DkYXRcoLLXh+V8PyI5CDq53pD+WPzYYncN8OZpHRKajKArCO3XFhHc+QlB4pOw45OBWfvYRbJUV4CLzRDWx7DkYVVXw0u8HkVdSKTsKObCb+0bg+wdi4O/pwqJHRKalKAq8A4Jw5xsz0bZPf9lxyIEVnc7B2rlf8Hci0d+w7DkQm6Zja2IOft2bJjsKOSiLquDl67rgrRu7w8Lz84jICSiKAouLC65/6nkMGHuz7DjkwGLXLEfKwf2czkn0Fyx7Dua/P3P6Jp2bu4uKzyb0wYSYVgC4fx4ROQ9FqXpz69JbJ2D01GdgdXWTHYkc1PLZ77PsEf0Fy56D0HSBT9cnIiGrSHYUckD+ni744YEYXBEdwpJHRE6t/YBBuOP1GfAJCpYdhRxQfuYpbP5hHs/dIzqDZc8BaLpAZmEZPlwTLzsKOaDwAA/8PuVSdG3px6JHRE5PURQEhUfizjdmIrBlhOw45IB2LfkVpzPSOMJHBJY9h2BRFTz/ywGUVXJDUKqpUwsf/D7lUoQHeLDoERGdoSgKPHx9cftr76B5m3ay45CD0TUbVn3+EVSLRXYUIulY9iSzaTpWx53CqrhM2VHIwcS0DcLPD13CFTeJiM5BURS4enji1pfeRHinrrLjkINJORiLw5vWcXSPnB7LnmSaEHjxt4OyY5CDGd29Bb69uz/crBYWPSKi81AUBRZXV9z43GuI6tlXdhxyMGu//gJaZSXP3yOnxrInkS4E3l8Vj5OnS2VHIQdyz6VR+PD23lC5tQIR0UUpigLVYsGYp19AdMxlsuOQAynOO42N330tOwaRVCx7kmi6QHJOCT7fkCg7CjmQ/1zTCc9f2xkAt1YgIqotRVGgqipGPfpvdLtihOw45ED2LFuMnJMnOJ2TnBbLniQWVcF/fo5FpcapBQQoCvD62K64f3Ab2VGIiAxLURQMf+Bh9L12rOwo5CCErmMlF2shJ8ayJ4FN0/Hr3lRsSciRHYUcgKoAb9/YHbf1j5QdhYjIFIaMvweDbhkvOwY5iLQjcTiwdhVH98gpsew1MSEEym06XlsSJzsKOQCrquCDW3thXO9wTtskImpEA2+4BVfc82DV1AlyeuvnfYXKinIu1kJOh2VPgreWH0FWYbnsGCSZi0XBJ7f3xqjuLVj0iIjsoOewazByyhNQVB7uOLvSgnxsXMDFWsj58NWvCWm6QGJWMb7dmiw7CknmYlEw+44+GNalOYseEZGdKIqCToOG4Ponn4PFxUV2HJJs/6qlyDuVAV3ndE5yHix7TciiKnh1ySFoOqcQOLOzRe+KTiEsekREdqYoCtr07ocb/vMKrC6usuOQRLqmYd3Xn0NVuVgLOQ+WvSZi03RsT8rB2iNZsqOQRC4WBbPu6M2iR0TUhBRFQUSnrrh26rNcldHJJezajpNxB7hYCzkNlr0mYrWoXJTFyVnVqnP0ruzEqZtERE2taoSvL0ZMeoyLtji5P+d+zvM4yWnwmd4EbJqO3/elYf/JfNlRSBJVAT68rReu6syiR0Qki6Io6HTZ5Rj6rwdkRyGJMpMScGj9nxzdI6fAstdE3lp+WHYEkmjamK64umsoix4RkWSKoqD31dci5sbbZUchiTYunMuFWsgpsOzZmaYLfL0lGSm5pbKjkCRPDO+A2wa0YtEjInIgl9x0O3qNvE52DJKkKDcHO35bBF3XZUchsiuWPTsrq9Tw4Zp42TFIkrsGtcbDV7SXHYOIiM7hin/dj86Dr5AdgyTZ8dtPKC8qhBAsfGReLHt2pOsCH/95DKdLKmVHIQnG9GyJF0d3kR2DiIguYMSDj6Ft3wGyY5AElWWl2LDgaygKD4fJvPjsthNdF8gprsD/NiXJjkISXB4djBk394AQ3FORiMiRKYqC6x7/DyK6dJMdhSQ48OdK5KSmcLEWMi2WPTtRVQVvLTuMskpODXA2fVoF4LMJfaAo4Hl6REQOTlEUKKqKG555Gc3btJMdh5qYEDo2zPuK+y+SabHs2YGmCxzLLMJPu0/KjkJNLLq5D765uz9cVJVFj4jIIBRFgcXFBTe98DoCW4bLjkNNLGHXdmQkxHN0j0yJZc8OLKqC15Ycgs4ZfE4lPMADC+4fAA9XC4seEZHBKIoCV3cP3PLSm/BpFiw7DjWxDfPncHSPTIllr5HZNB3bEnOw9kiW7CjUhPw8XLDw/oEI8HRl0SMiMihFUeDh44tbX3oTnn7+suNQEzpxYB9SDsZydI9Mh2WvkVktKl5bEic7BjUhq6rgs/F90NLfg0WPiMjgFEWBT7NgjPn3C7BYrbLjUBPasICje2Q+LHuNyKbpWHEwA7Gp+bKjUBN68bou6B8VyKJHRGQSiqIgtG17XHH3g7KjUBNKjz+CxN07oGs22VGIGg3LXiOyWlS8v5obqDuT8QNbYfzAVix6REQmoygKul85Aj2GjZQdhZrQpu+/hWrhiC6ZB8teI7FpOtYeycTBtALZUaiJXNquGV6+jpumExGZ2RV3P4iWnfha7ywykxJwbMdWju6RabDsNRKrRcUHHNVzGlHNvPDZ+Kq99IiIyLwURcGYf78AnyCu0OksNv8wj6N7ZBose43ApunYmpiD3SfyZEehJuDrYcU3d/fnFgtERE5AURS4eXhi3H9ehtXVTXYcagJZyUk4um0TR/fIFFj2GoHVouL9VRzVcwYWVcGnd/ZBywCuvElE5CwURUFgywhc/dBjsqNQE9n8w3yO7pEpsOw1kE3XsefEaWxJzJEdhZrAC9d2xsA2QSx6RERORlEURMdchn7XjZMdhZpATkoyEnZu4+geGR7LXgNZVY7qOYvb+kdg4iWtWfSIiJzYZbf/C6179JYdg5rAtl++5+geGR7LXgNouo5DaQVYezRLdhSysy5hvnj1+q4QQsiOQkREkl33xH8R0CJMdgyys/T4IzgZdwC6psmOQlRvLHsNYFFVvL/6qOwYZGc+blZ8ObEvLKrCUT0iIienKAqsrq4Y959X4erhITsO2dnWRd9BtVhkxyCqN5a9etJ0gYTMIqw4dEp2FLKzt2/qjua+7ix6REQEoKrw+QaH4JpH/w3uwWNuyfv3IDM5CbrO0T0yJpa9erKoCt5fHQ/O6jO38QNb4equLVj0iIioBkVR0KZnX1xy0x2yo5CdbVu0EKrK0T0yJpa9etCFwIncEizenyY7CtlR15a+eHF0Z9kxiIjIQSmKgphxt6JV916yo5AdxW/bgrxT6RC6LjsKUZ2x7NWDqij4cE08dI7qmZaPmxVfTOgHi8oRPSIiOj8hBEY9/CTcvX1kRyE7EULHtp+/h6LysJmMh8/aOtKFQEZ+GX7Zkyo7CtnRuzf1QHNfN07fJCKiC1IUBe4+vhh+/8Oyo5AdxW34E8V5p7kqNxkOy149zFqXgEqNP+xmNfGS1hjeNZRFj4iIakVRFLQfcAk6D75CdhSyE81mw47ffgIXayCjYdmro7JKDT/uTJEdg+yke7gfXhjVSXYMIiIyGCEEht03BX4hzWVHITvZv2oZKsvLZMcgqhOWvTqwaTq+25GC4gouv2tGnq4WzL6zD1Sep0dERHWkKAosLi649rFneG6XSVWWl2HfyqXcZJ0Mha9GdWC1qPh6S7LsGGQnz47siBZ+3E+PiIjqR1EUNG/TDv2vv1F2FLKTvSuW8DiBDIVlr5Zsmo71R7OQlF0sOwrZwaXtmmF8TGu+gBMRUYMoioJLbr4Tzdu2lx2F7KAgKxPHdm6DptlkRyGqFZa9WrJaVPxvU5LsGGQHPm5WvH9rT66wRUREjUJRFFz3+H9gdXOTHYXsYPfS32CxWGXHIKoVlr1a0IVASm4J1h3Nkh2F7ODF0Z0R6OXKUT0iImoUiqLAJ6gZhtx5j+woZAcnD8Ui5+QJbrJOhsCyV0v/25TE1XZN6KpOIbixbwSLHhERNSpFUdBz+DWI6tVXdhSyg11LfgF47EAGwLJXCxU2HT/uPCk7BjUyf08XvHsTp28SEZF9CCFwzcNPwsPXT3YUamRxG9ehorREdgyii2LZuwibpuP7nSkoLOeJuGbz2piu8PWwclSPiIjsQlEUuHl6YcSDj8qOQo3MVlGOvSv+4DYM5PBY9i7CalExdzO3WzCba7u3wLXdw1j0iIjIrhRFQdve/dHtyhGyo1Aj27fiD07lJIfHsncBNk3H5mPZSMgqkh2FGlGwtxum39CN0zeJiKhJCCEwdOJ98PIPkB2FGlFhThaO7djCbRjIobHsXQC3WzCnV67vAi83Tt8kIqKmoSgKrK5uGDKeq3OaDbdhIEfHsnceQgik5ZVizeFM2VGoEQ3pEIyR3Vqw6BERUZNSFAWdLr0c4Z27yY5CjSg17iBOZ6RzthA5LJa98xAC+GrTcej82TUNN6uKN8dx+iYREckhhMCISY9CtVhkR6FGtH/lHzy2IIfFsncelXrVKpxkHpOGtEVzX3eO6hERkRSKosAvpDl6j7xOdhRqRAfXrwE3YyZHxbJ3DpWajt/3pSG/tFJ2FGokrYI88fAV7Vj0iIhIKkVRMOjWCfAODJIdhRpJaUE+EnZug86FWsgBseydg4tFxU+7UmXHoEb0+tiusKgsekREJJ/FasUV/5okOwY1ov2rl0HlQi3kgFj2ziEjvwxbk3Jkx6BGMrJrKAa1C+aoHhEROQRFUdB+QAxade8lOwo1kuT9e1F0Old2DKJ/YNn7G9uZc/U49docvFwteG1MV544TUREDkUIgeEPPAKLlaNBZiCEjtjVy6BrmuwoRDWw7P2NVVXx8x5O4TSLR6/qgEAvV47qERGRQ1EUBT5BzdDn2rGyo1AjObB2FRSVh9bkWPiM/AtNF9ibchpJ2cWyo1AjiG7ug3sujWLRIyIih6QoCi656Xb4BofIjkKNoCArEydi93F0jxwKy95fKArw/Y6TsmNQI5l2Q1dwTRYiInJkqsWKK+/iYi1msX/1Uu6jSA6FZe8vNE1gcWya7BjUCK7qFIK+rQI5qkdERA5NURS06dMfUb36yo5CjSBh5zaUFRXJjkFUjWXvDJumY2XcKRSUco8Uo7OoCl64tjMXZSEiIkMQQmDY/Q/D6uIqOwo1kGazIW7jWmg2Hk+SY2DZO8NqUfHDTk7hNIOb+oQjMsiLo3pERGQIiqLAOyAQfUZzsRYzOLxpLVdZJYfBsnfG6eIKbIjPkh2DGsjDxYKnR3bkqB4RERmKoigYMOYmePj4yo5CDZR29DAKc7JlxyACwLIHoGoK50+7T8KmsyAY3T2XRcHfw4WjekREZDhWVzf0v/5G2TGoERza8CdX5SSHwLKHqimcP+3mFE6jC/JyxcND27HoERGRISmKgl4jr4N3YJDsKNRAhzeu5aqc5BCcvuzpQuBIRiHi0gtlR6EGevjK9nC1Ov1TmoiIDEy1WBBz4+2yY1ADZackIyc1haeVkHQ8MhbA9ztTZKegBmod5InxA1txVI+IiAxNURR0GzoMAS1ayo5CDXRo/RoIocuOQU7O6cueAPDr3lTZMaiB/n11R26gTkRE5qAouPTWCbJTUAMd3rQeqsqpnCSXU5c9TRfYlpiD7KIK2VGoAXpG+OOabi04qkdERKagKAo6DByEkKi2sqNQAxRknUJ6/BHoOkf3SB6nLnuKAvxxIEN2DGqgJ4Z34Jx4IiIyFSEER/dM4ND6NeBb0SSTU5c9VVGw8hDLnpF1a+mHy9oHc1SPiIhMRVEURPXsg+Zt28uOQg1wZOtG8O1okslpy54uBPafzMOpgnLZUagBHruqPUf1iIjIlIQQuPTm8bJjUAOUFuTj5KFYTuUkaZy27AkB/BGbLjsGNUB0cx9c2ak5R/WIiMiUFEVB6569ObpncPHbNnMqJ0njtGXPoipYcfCU7BjUAA9f2Y6jekREZGpCCAy66U7ZMagBju3cCkV12kNukswpn3lCCCRlFyMxu1h2FKqnNs28uAInERGZnqIoiOrFc/eMrCg3B6eSEvgGNUnhlGVP0wWW7OcUTiN76PK2nBJBREROoWp07w7ZMagBjm7dCMHz9kgCpyx7VouK5Qe5CqdRhQd4YGzvcI7qERGRU6ga3evLffcM7NiOrVAt3GCdmp5Tlr3MwjLEpubLjkH19ODlbaGy5xERkRMRQqDPNdfLjkH1lJuagvxMDjRQ03O6slep6fgjlj9sRhXq645b+kZwVI+IiJyKoijoOGgIPP38ZUehejqyZSM0zSY7BjkZpyt7LpzCaWj3DY6CymE9IiJyQoqqovtVV8uOQfV0bMdWWCxW2THIyThd2Ssoq8T2pFzZMagevFwtuGNAJFSO6hERkRNSFAV9Ro2BysJgSOnHjqC0sEB2DHIyTlX2bJqOFQczoOlc+taIxvUJh5uVJzcTEZHzcvfyRoeYS2XHoPoQAke3bYJm41ROajpOVfaqVuHkRupGpCjAA4PbyI5BREQklRACA66/SXYMqqeEHdtgsXJklpqOU5W9cpuGDfFZsmNQPVzarhlaBnhyYRYiInJqiqKgWWQrhLbrIDsK1UPKoVhotkrZMciJOE3Z03SBDfHZKKvkhpZGdM+lURCC02+JiIiEEOgzaozsGFQPtopynIw7CJ0brFMTcZqypyjAhvhs2TGoHiICPTC4QzBH9YiIiFA1utdh4KXwCgiUHYXq4fjeXbIjkBNxmrKnKgo2HWPZM6IJMa3BmkdERPT/FEVBj6tGyo5B9XB8326oqtMcgpNkTvNMyykqx7HMItkxqI48XCy4o38kR/WIiIj+QlEU9LrmOi72YUDZKckoyc+THYOchFOUPZumYz0XZjGksb1awsOV2y0QERH9nbunF6IvGSw7BtVD4p4d3IKBmoRTlD2LqmAjz9czpPsui5IdgYiIyCEJIdB39A2yY1A9JO/bw1FZahJOUfYURcGmYzmyY1AdDWwTiKhgb07hJCIiOgdFURAc2RphHTrKjkJ1lBy7l6uMU5NwirKXnFOMjIIy2TGojm7uG8EXQiIiogsQQqD3NWNkx6A6Ki0sQNbxRB7nkN2ZvuxVajrWHeX5ekbj6WrBtd1bcFSPiIjoAhRFQfv+MfDw8ZUdheoocc8OCF2THYNMzvRlz8WicgqnAY3sGgpXKxdmISIiuhhFVdF+wCDZMaiOju/fA9XC8/bIvkxf9nQhsCWRi7MYza39Izm1gYiIqJa6XH6l7AhUR+lHD8NWUSE7BpmcqcueEAJx6QUoKOXStkbS0t8D/VoHcgonERFRLSiKgrD2HeETFCw7CtWBrmlIiz8Moeuyo5CJmbrs2XSBdUd4vp7RjOnVkqN6REREdSCEQMdB3HPPaE4eiuUxD9mVqcuei0XFpgRO4TSa2wdEyo5ARERkOF2HDpMdgeroZNxBqBauUUD2Y+qyV2HTsfP4adkxqA56Rfijpb8Hp3ASERHVgaIoCAwLR1A43zA1kvT4I9A1rshJ9mPasqfrArtPnEa5jfOgjWRcn3BOZyAiIqoHTuU0HltFOTKTEnjsQ3Zj2rInAKzn/nqG4mZVcUOvlhzVIyIiqgdFUdB16HDZMaiOThzcz9E9shvTlj2LqmBXMqdwGskVHUPg6cb9ZoiIiOrLOyAQLdpHy45BdZB6+CAsVh7/kH2YtuzpukBsar7sGFQH1/YI4zQGIiKiBqiayjlEdgyqg9Qjh3j8Q3Zj2rKXlF2MkgoOiRuFm1XFVR1DOIWTiIioARRFQZchV0JRTXuIZzrlxcXITU2RHYNMypSvBJWajh3JubJjUB0MatcMbi5cepiIiKih3Dy9ENm1h+wYVAcnDuyHZrPJjkEmZMqyZ1EV7EvJkx2D6mBEl1BOYSAiImoEnMppPCfjDvC8PbILUz6rVEXBvhSer2cUFlXBqO4tOIWTiIioESiKgo4xl2H1F5/AVlkhOw7VQvqxI7IjNDohBGw2GzSuNNroXFxcYLHUbkacKcteuU3DkVOFsmNQLfVpFQBvrsJJRETUaKxubojq1Rfx2zfLjkK1UJidhbLiIrh7ecuO0igqKiqQnp6OkpIS2VFMSVEUhIeHw9v74s8X0x1hCyFwILUAms4pgUYxoktzCCE4skdERNRIhBBoP+ASlj0DSY8/gtbdexl+cR1d15GUlASLxYKwsDC4urryGK8RCSGQlZWFkydPon379hcd4TNd2bPpArtPcH89I7muBzdSJyIiakyKoqBNr76AogA8J94QMo4dRWTXHrAYvOxVVFRA13VERETA09NTdhxTCg4OxvHjx1FZWXnRsmfsZ9M5uFhULs5iIF3CfBHs4yY7BhERkem4eXkjpHUb2TGolk4lxptqkRbV4KXVkdVlkMSU/wp7WfYMYzhX4SQiIrILIQRa9+gtOwbVUkbiMdkRyIRMV/bySipw8nSp7BhUS9d2C5UdgYiIyLTa9O4vOwLVUvHpXJTk58mOQQ20du1aKIqCvLw82VEAmKzs6brAnhN5smNQLYUHeKBtiA/P1yMiIrIDRVHQon00XNw9ZEehWkqPPwJd12XHaDIZGRl4+OGH0aZNG7i5uSEiIgKjR4/G6tWra/X1c+bMgb+/v31D1tEll1yC9PR0+Pn5yY4CwGxlTwjsSeHiLEYxqF0zTuEkIiKyI1VVEdG5m+wYVEsZCfFOs6DO8ePH0adPH6xZswZvv/02YmNjsWzZMgwdOhSTJ0+WHa9eKisr4erqitDQUIcZzDBV2bNaVG6mbiCXtQ+SHYGIiMjUhBBo3bOP7BhUSxmJ8VBruVm20T300ENQFAXbt2/HuHHj0KFDB3Tp0gWPP/44tm7dCgCYMWMGunXrBi8vL0REROChhx5CUVERgKrpknfddRfy8/OhKAoURcFLL70EACgvL8eTTz6Jli1bwsvLCwMGDMDatWtr3P/nn39evWLo2LFjMWPGjH+MEs6aNQtt27aFq6sroqOj8c0339S4XFEUzJo1C9dddx28vLwwbdq0c07j3LhxIy677DJ4eHggIiICjzzyCIqLi6sv/+STT9C+fXu4u7ujefPmuPHGGxvnmwyTlT2Ai7MYhaIAQzqEOMy7HkRERGakKAra94+RHYNq6VRCvOwITSI3NxfLli3D5MmT4eXl9Y/Lz5YuVVXxwQcf4ODBg5g7dy7WrFmDf//73wCqpkvOnDkTvr6+SE9PR3p6Op588kkAwJQpU7BlyxYsXLgQ+/fvx0033YSrr74a8fFV399NmzZh0qRJePTRR7F3714MGzYM06ZNq5Hh559/xqOPPoonnngCBw4cwAMPPIC77roLf/75Z43rvfTSSxg7dixiY2Nx9913/+OxJCQk4Oqrr8a4ceOwf/9+fPfdd9i4cSOmTJkCANi5cyceeeQRvPLKKzhy5AiWLVuGwYMHN+wb/BeKMNE8urS8UlwyfY3sGFQLHUN9sOyxxnsiExER0fl98fA9yM88JTsG1cIDs+bCO9C4s5/KysqQlJSEqKgouLu7n/M627dvx4ABA7Bo0SKMHTu21rf9448/YtKkScjOzgZQdc7eY489VmMU7cSJE2jTpg1OnDiBsLCw6s9fddVV6N+/P15//XXceuutKCoqwuLFi6svv/POO7F48eLq2xo0aBC6dOmCzz77rPo6N998M4qLi7FkyRIAVW+mPPbYY3jvvfeqr7N27VoMHToUp0+fhr+/P+69915YLBZ8+umn1dfZuHEjhgwZguLiYvzxxx+46667cPLkSfj4+NTq+1Cb7/FZphnZ03WBQ+kFsmNQLfF8PSIioqYhhECr7tyCwSgyk5MgTL5IS22PAVetWoUrr7wSLVu2hI+PD8aPH4+cnByUlJSc92tiY2OhaRo6dOgAb2/v6j/r1q1DQkICAODIkSPo37/mSrV//zguLg6DBg2q8blBgwYhLi6uxuf69u17wcewb98+zJkzp0aWESNGQNd1JCUlYdiwYWjVqhXatGmD8ePHY968eRd8fHVlmp0bNSEQf6pQdgyqpSEdmsmOQERE5DTa9OqL/auWyo5BtZB94jhadesJi4k3JW/fvj0URcHhw4fPe53jx4/j2muvxYMPPohp06YhMDAQGzduxD333IOKigp4enqe8+uKiopgsViwa9cuWP52/qO3t3ejPg4A55yG+vc8DzzwAB555JF/XBYZGQlXV1fs3r0ba9euxYoVK/DCCy/gpZdewo4dOxplpVHTPItcLCqOZRbJjkG1YFUVDGwTxPP1iIiImoCiKGjVvZfTLPxhdDknT8BiNc14zDkFBgZixIgR+Pjjj2ssVHJWXl4edu3aBV3X8e6772LgwIHo0KED0tLSalzP1dUVmqbV+FyvXr2gaRoyMzPRrl27Gn9CQ6v2d46OjsaOHTtqfN3fP+7UqRM2bdpU43ObNm1C586d6/RYe/fujUOHDv0jS7t27eDq6goAsFqtuOqqq/DWW29h//79OH78ONasaZxT00xT9gCw7BlEzwh/uFr5C4eIiKipWF1d0aJdtOwYVAvZKcmyIzSJjz/+GJqmoX///vjpp58QHx+PuLg4fPDBB4iJiUG7du1QWVmJDz/8EImJifjmm28we/bsGrfRunVrFBUVYfXq1cjOzkZJSQk6dOiAO+64AxMmTMCiRYuQlJSE7du344033qg+1+7hhx/GH3/8gRkzZiA+Ph6ffvopli5dWmMg4qmnnsKcOXMwa9YsxMfHY8aMGVi0aFH1IjC19fTTT2Pz5s2YMmUK9u7di/j4ePz666/VC7QsXrwYH3zwAfbu3Yvk5GR8/fXX0HUd0dGN8/PKskdNjufrERERNS0hBFr14Hl7RpCbetIpjpPatGmD3bt3Y+jQoXjiiSfQtWtXDBs2DKtXr8asWbPQo0cPzJgxA2+++Sa6du2KefPm4Y033qhxG5dccgkmTZqEW265BcHBwXjrrbcAAF999RUmTJiAJ554AtHR0RgzZgx27NiByMhIAFXn3s2ePRszZsxAjx49sGzZMkydOrXGYidjxozB+++/j3feeQddunTBp59+iq+++gqXX355nR5n9+7dsW7dOhw9ehSXXXYZevXqhRdeeKF68Rh/f38sWrQIV1xxBTp16oTZs2djwYIF6NKlSwO+u//PNKtxZhWWo9+0VbJjUC0seugS9Irw5zROIiKiJiKEQGZyEr59+p/nDZHjue/jr+DbLFh2jHqpy0qRjuS+++7D4cOHsWHDBtlRLqou32NTTAgWQuAIF2cxBE9XC3qE+7HoERERNSFFURDSKgquHp6oKG28lf7IPrKSE+EdGATVxIu0yPbOO+9g2LBh8PLywtKlSzF37lx88sknsmM1OlM8g2yawNEMlj0j6BHub+rVpYiIiByVoiho3qat7BhUC9knkk2//YJs27dvx7Bhw9CtWzfMnj0bH3zwAe69917ZsRqdKUb2LBaF5+sZRI8IPwghOLJHRETUxIQQaN6mPVIOxsqOQheR7QQrcsr2/fffy47QJEwxxKIqCo5lsewZQZ9WAbIjEBEROa0W7bkipxHkOMmKnGR/pih7ALihukH0bRXIUT0iIiIJFEVBy4512yOM5MhNO8lpnNQoTFH28ksrcbqkUnYMuojmvm4I8HKVHYOIiMhpefkFwN3bR3YMugitshIFOVmyY5AJmKLscVTPGHpG+MuOQERE5PSat2knOwLVwum0VKfYb4/sy/Blr1LTcYQrcRpCj3B/vmgRERFJVLVIC8ueEeSdSoeuabJjkMEZvuxZuDiLYXBxFiIiIvnC2neUHYFqIe9UBtc5oAYzfNlTVW67YASKUrXtAl+0iIiI5FEUBWHRnWTHoFrIP5UB1WKRHYMMzvBlDwASs4plR6CLaBvsDXcX7hdDREQkm4ePLzz9/GXHoIvIz8yQHaHRaXrTnc5T1/vSNA3PP/88oqKi4OHhgbZt2+LVV1897ylIkyZNgqIomDlzZo3Pt27dGoqi1Pgzffr0+j6MBjP80beuC2QUlMmOQRfRM8Kfm6kTERE5iOZt2iFpz07ZMegC8k6Zr+xZVAWPLtxj91l57UK88f6tver0NW+++SZmzZqFuXPnokuXLti5cyfuuusu+Pn54ZFHHqlx3Z9//hlbt25FWFjYOW/rlVdewX333Vf9sY+PvBVwDV/2cosrmvRdAqqfHuH+siMQERERqhZpCW3bgWXPwVWWlaKsqNB0W2UcyyzCwbQC2TH+YfPmzbj++usxatQoAFUjdAsWLMD27dtrXC81NRUPP/wwli9fXn3dv/Px8UFoaKjdM9eG4adxpuWXyo5AtdAlzIejekRERA6iRbsOsiNQLZhxdM9RXXLJJVi9ejWOHj0KANi3bx82btyIkSNHVl9H13WMHz8eTz31FLp06XLe25o+fTqCgoLQq1cvvP3227DZbHbPfz6GHtnTdIGUXJY9I+gQ6is7AhEREeHMIi0duEiLEZxOT0VI6zZcqKUJPPPMMygoKEDHjh1hsVigaRqmTZuGO+64o/o6b775JqxW6z+mdf7VI488gt69eyMwMBCbN2/Gs88+i/T0dMyYMaMpHsY/GL7spXNkz+EFernC283QTzUiIiJTcfPygndgEIpyc2RHoQvIO5XBPYqbyPfff4958+Zh/vz56NKlC/bu3YvHHnsMYWFhmDhxInbt2oX3338fu3fvvuBstccff7z67927d4erqyseeOABvPHGG3Bzc2uKh1KDoadxWlQF6flcnMXRtQvxlh2BiIiI/ia0LadyOrr8zAxYrHzDvCk89dRTeOaZZ3DrrbeiW7duGD9+PKZOnYo33ngDALBhwwZkZmYiMjISVqsVVqsVycnJeOKJJ9C6devz3u6AAQNgs9lw/Pjxpnkgf2PoZ09V2ePInqNrF+zNlTiJiIgciBACgS3DgR2yk9CFFGSdkh3BaZSUlEBVa46DWSwW6LoOABg/fjyuuuqqGpePGDEC48ePx1133XXe2927dy9UVUVISEjjh64FQ5c9AEjP48ieo+PIHhERkePxC3GM1QLp/Ipyc2VHcBqjR4/GtGnTEBkZiS5dumDPnj2YMWMG7r77bgBAUFAQgoKCanyNi4sLQkNDER0dDQDYsmULtm3bhqFDh8LHxwdbtmzB1KlTceeddyIgIKDJHxNghrLHaZwOL7qFuZYMJiIiMjpFURDQ4tx7hJHjKM4zX9lrikGA+tzHhx9+iOeffx4PPfQQMjMzERYWhgceeAAvvPBCrW/Dzc0NCxcuxEsvvYTy8nJERUVh6tSpNc7ja2qGLnuaLpBZyLLn6DqF+nIKJxERkYMJahkhOwJdREVpKSoryuHi2vQLe9iDpos6b3bekPuyqLU//vTx8cHMmTMxc+bMWn/N38/D6927N7Zu3Vrrr28Khl6gJaeoHNxP3bF5u1kR6OUqOwYRERH9jYePL1SLod/3dwol+fmyIzSaupQvI92XIzN02UvjFE6H1zbYS3YEIiIiOgdFVeHbLFh2DLqIopxs2RHIwAxb9jRdx8ncEtkx6CK4OAsREZHj8mvORVocXWFuNnRNkx2DDMrAZY8je0bQNtgbOjcDJSIicjhCCPiz7Dm8otxcCKHLjkEGZdiyZ1EVZHCPPYfXMsADnDFNRETkmLj9guMrzsuFohj2kJ0kM+wzx6IqHNkzgFaBnlyJk4iIyAEpioKAMK7I6eiKT+dCtVhkxyCDMmzZA4DMApY9Rxce6CE7AhEREZ1HswiWPUdXdNp8e+1R0zF02TtdUik7Al2AogABHtx2gYiIyFF5BQTJjkAXYcaN1anpGLrsFZSy7DmyZl5usFgM/RQjIiIyNRdXV3j4+MqOQRdQkpcnOwIZmKGPxAvLbLIj0AW08HeXHYGIiIgugou0OLbyEm41RvVn2LJXbtNQoXEZWkfWwo9lj4iIyNFxrz3HJoSOilKTrECvN+F+gXW8rzfeeAP9+vWDj48PQkJCMGbMGBw5cqTGdS6//HIoilLjz6RJk6ovz8nJwdVXX42wsDC4ubkhIiICU6ZMQUFBQaM8pPqwSrvnBiriqJ7Da+HnASEEV+MkIiJyUELo8AtpLjsGXUR5aTFcPUyw6J1qAX66F8g+at/7adYBGPdFnb5k3bp1mDx5Mvr16webzYb//Oc/GD58OA4dOgQvL6/q691333145ZVXqj/29PSs/ruqqrj++uvx2muvITg4GMeOHcPkyZORm5uL+fPnN/xx1YNhyx6ncDq+Fn7uEAD32SMiInJYCjdWN4Dy4mL4BDaTHaNxZB8F0vfJTvEPy5Ytq/HxnDlzEBISgl27dmHw4MHVn/f09ERo6Ll/ZgICAvDggw9Wf9yqVSs89NBDePvtt+0TuhYMO40zn4uzOLwWfu4sekRERA7O3ZsLtDi60sJC2RGcTn5+PgAgMDCwxufnzZuHZs2aoWvXrnj22WdRcoFzKtPS0rBo0SIMGTLErlkvxLAje6dLKmRHoIuI4IbqREREDk1RFHj4+MiOQRdRVlTAU2OakK7reOyxxzBo0CB07dq1+vO33347WrVqhbCwMOzfvx9PP/00jhw5gkWLFtX4+ttuuw2//vorSktLMXr0aHzxRd2mlDYmQ5Y9Tdc5smcALf1NMLeciIjI5Dz9AmRHoIsoKyqCrmmwWA156G44kydPxoEDB7Bx48Yan7///vur/96tWze0aNECV155JRISEtC2bdvqy9577z28+OKLOHr0KJ599lk8/vjj+OSTT5os/18Z8hmjC6CglOfsOTpfd0M+vYiIiJyKu7e37Ah0EeUlxQCE7BhOYcqUKVi8eDHWr1+P8PDwC153wIABAIBjx47VKHuhoaEIDQ1Fx44dERgYiMsuuwzPP/88WrRoYdfs52LIo3EhgIIyjuw5MlUB3F0N+fQiIiJyKi7unInj6MqKi8Al7+xLCIGHH34YP//8M9auXYuoqKiLfs3evXsB4IIlTtertoorLy9vlJx1ZcijcVUBCjiN06F5c1SPiIjIEFxcXaEoKoTg/sWOqryoCKpq2HUVDWHy5MmYP38+fv31V/j4+CAjIwMA4OfnBw8PDyQkJGD+/Pm45pprEBQUhP3792Pq1KkYPHgwunfvDgD4448/cOrUKfTr1w/e3t44ePAgnnrqKQwaNAitW7eW8rgMeURuURWO7Dk4X3cX2RGIiIioltw8Pc+MHpEjKisphmKWstesg0Pex6xZswBUbZz+V1999RX+9a9/wdXVFatWrcLMmTNRXFyMiIgIjBs3Ds8991z1dT08PPD5559j6tSpKC8vR0REBG644QY888wzDXo4DWHIsqcoCs/Zc3B+Hix7RERERuHm7c2y58AqSoplR2gculbnzc4bdF+qpdZXF+LC50RGRERg3bp1F7zO0KFDsXnz5lrfZ1Mw7FsEHNlzbBzZIyIiMg53Ly7S4shslSbZcqwO5ctQ9+XAjFv2eM6eQ/PhOXtERESG4eblJTsCXYBWyRltVD+GLXuFZXzSOzJfTuMkIiIyDI7sOTbTjOxRkzNs2Sur1GRHoAvwdbdedO4zERERySeEgJsny54j0ypY9qh+DFv2KnUWCUfm6+HCrT+JiIgMghurOzZbJU9fovoxbNmzadwLxpFxgRYiIiLjcPPkOXuOTGPZo3oybNmr1Dhu5Mh8PaxQZIcgIiKiWnHjOXsOjefsUX0ZuOxxZM+RuVoa76k1ffp0KIqCxx57rPpzCQkJGDt2LIKDg+Hr64ubb74Zp06duuhtpaam4s4770RQUBA8PDzQrVs37Ny5s/ryd955ByEhIQgJCcG7775b42u3bduGPn36wGbj4kBERGQunMbp2DSWPaonlj2yCxdr4zy1duzYgU8//RTdu3ev/lxxcTGGDx8ORVGwZs0abNq0CRUVFRg9ejR0/fzPi9OnT2PQoEFwcXHB0qVLcejQIbz77rsICAgAAOzfvx8vvPACFi5ciAULFuC5555DbGwsAMBms2HSpEmYPXs2rFZuK0FEROahKArcPDxlx6ALsFVwGifVjyGPWnUhwPVZHJurRYWiNGwiZ1FREe644w58/vnneO2116o/v2nTJhw/fhx79uyBr68vAGDu3LkICAjAmjVrcNVVV53z9t58801ERETgq6++qv5cVFRU9d8PHz6M7t2744orrgAAdO/eHYcPH0a3bt3w9ttvY/DgwejXr1+DHhMREZFDauDvbLIvXeOsIqofQ47s2Xi+nsNza4SRvcmTJ2PUqFH/KG/l5eVV70K6uVV/zt3dHaqqYuPGjee9vd9++w19+/bFTTfdhJCQEPTq1Quff/559eXdunXD0aNHceLECSQnJ+Po0aPo2rUrEhIS8NVXX9UonERERGbS0Ddoyf7MsEiLpjfd1ml1va9Zs2ahe/fu8PX1ha+vL2JiYrB06dLqy8vKyjB58mQEBQXB29sb48aN+8cpRCdOnMCoUaPg6emJkJAQPPXUUzVO/1m0aBGGDRtWfRpSTEwMli9f3rAHehGGHdkjx+ZmtTTo6xcuXIjdu3djx44d/7hs4MCB8PLywtNPP43XX38dQgg888wz0DQN6enp573NxMREzJo1C48//jj+85//YMeOHXjkkUfg6uqKiRMnolOnTnj99dcxbNgwAMAbb7yBTp064aqrrsJbb72F5cuX46WXXoKLiwvef/99DB48uEGPkYiIyFFwb1zHp2kaLC7GXu3colrwzPpnkJifaNf7aePXBtMHT6/T14SHh2P69Olo3749hBCYO3curr/+euzZswddunTB1KlTsWTJEvzwww/w8/PDlClTcMMNN2DTpk0Aqv59Ro0ahdDQUGzevBnp6emYMGECXFxc8PrrrwMA1q9fj2HDhuH111+Hv78/vvrqK4wePRrbtm1Dr169Gv37ALDskZ00ZIGWlJQUPProo1i5ciXc3d3/cXlwcDB++OEHPPjgg/jggw+gqipuu+029O7dG6p6/vvVdR19+/at/oHr1asXDhw4gNmzZ2PixIkAgEmTJmHSpEnVXzN37lz4+PggJiYG0dHR2LFjB06ePIlbb70VSUlJNUYXiYiIjIsje47OLIOvifmJiMuNkx3jH0aPHl3j42nTpmHWrFnYunUrwsPD8eWXX2L+/PnVp/t89dVX6NSpE7Zu3YqBAwdixYoVOHToEFatWoXmzZujZ8+eePXVV/H000/jpZdegqurK2bOnFnjPl5//XX8+uuv+P333+1W9gw5jZO7dTs+i6X+r0i7du1CZmYmevfuDavVCqvVinXr1uGDDz6A1WqFpmkYPnw4EhISkJmZiezsbHzzzTdITU1FmzZtznu7LVq0QOfOnWt8rlOnTjhx4sQ5r5+dnY2XX34ZH374IbZt24YOHTqgffv2GDp0KCorK3H06NF6P0YiIiLHwoMrx2eStmcAmqZh4cKFKC4uRkxMDHbt2oXKysoapxZ17NgRkZGR2LJlCwBgy5Yt6NatG5o3b159nREjRqCgoAAHDx485/3ouo7CwkIEBgba7bEYcmSPL0eOryHvPl155ZXVq2Ceddddd6Fjx454+umnYbH8/xTRZs2aAQDWrFmDzMxMXHfddee93UGDBuHIkSM1Pnf06FG0atXqnNefOnUqpk6divDwcOzYsQOVf5krb7PZoGlNN++ciIjIrnhw5fjY9ewuNjYWMTExKCsrg7e3N37++Wd07twZe/fuhaurK/z9/Wtcv3nz5sjIyAAAZGRk1Ch6Zy8/e9m5vPPOOygqKsLNN9/c+A/mDJY9souGvB75+Piga9euNT7n5eWFoKCg6s+fHToPDg7Gli1b8Oijj2Lq1KmIjo6u/porr7wSY8eOxZQpUwBUlbdLLrkEr7/+Om6++WZs374dn332GT777LN/ZFi5ciWOHj2KuXPnAgD69euHw4cPY+nSpUhJSYHFYqlxX0REREYmeHTl8BS2PbuLjo7G3r17kZ+fjx9//BETJ07EunXr7HJf8+fPx8svv4xff/0VISEhdrkPwKhlj+fsOTx7r+p15MgRPPvss8jNzUXr1q3x3//+F1OnTq1xnYSEBGRnZ1d/3K9fP/z888949tln8corryAqKgozZ87EHXfcUePrSktLMWXKFHz33XfV5wCGh4fjww8/xF133QU3NzfMnTsXHh4edn2MRERETYfHVg6PXc/uXF1d0a5dOwBAnz59sGPHDrz//vu45ZZbUFFRgby8vBqje6dOnUJoaCgAIDQ0FNu3b69xe2dX6zx7nbMWLlyIe++9Fz/88MN5twxrLAYte7IT0MU0dtdbu3ZtjY+nT5+O6dMvvMrS8ePH//G5a6+9Ftdee+0Fv87Dw+Mf0z0B4N5778W999570axERERGw2Mrx8eRvaan6zrKy8vRp08fuLi4YPXq1Rg3bhyAqoGHEydOICYmBgAQExODadOmITMzs3qkbuXKlfD19a2xZsSCBQtw9913Y+HChRg1apTdH4Mxy57sAHRx/EciIiIyDqHLTkAk1bPPPouRI0ciMjIShYWFmD9/PtauXYvly5fDz88P99xzDx5//HEEBgbC19cXDz/8MGJiYjBw4EAAwPDhw9G5c2eMHz8eb731FjIyMvDcc89h8uTJ1au3z58/HxMnTsT777+PAQMGVJ/L5+HhAT8/P7s8LmOWPb795PAqufE9ERGRYfDYygBMMrDXxu/8K6fLvI/MzExMmDAB6enp8PPzQ/fu3bF8+fLq/Zffe+89qKqKcePGoby8HCNGjMAnn3xS/fUWiwWLFy/Ggw8+iJiYGHh5eWHixIl45ZVXqq/z2WefwWazYfLkyZg8eXL15ydOnIg5c+bU/wFfgCHLnkU1ybPdxEoruFIlERGRUXBgzwiMf/yr6VqdNztvyH1ZVMvFr3jGl19+ecHL3d3d8fHHH+Pjjz8+73VatWqFP/7447yX//20pKZgyH323Ky1/4cjOUoqbLIjEBERUS0JsO05OjNsql6X8mWk+3Jkhix7rlZDxnYqxSx7REREhiG4d6wBmKDtUZMzbGtyY+FzaGWVOuf/ExERGYAQgstxGoC9t7UiczJsY3J34dCsIyur5DuERERERsGq59gsVisU1bCH7SSRYZ817i6Gje4Uymwse0REREYhdJ6z58isrm6yI5BBGbYxeXBkz6GVVep8l5CIiMggykuKZUegC7C6sexR/Ri27HEap2PjNE4iIiKjECgvZtlzZC4c2aN6Ytkjuyhl2SMiIjIMjuw5No7sUX0ZuOwZNrpTKKvUuUAwERGRISgsew7OhWWP6smwjYkje46tvFLjEsFEREQGoCgKp3E6OC7QQvXFskd2UVBWKTsCERER1RJH9hybWUb2hNZ0p/nU577Wr1+P0aNHIywsDIqi4JdffvnHdeLi4nDdddfBz88PXl5e6NevH06cOAEAyM3NxcMPP4zo6Gh4eHggMjISjzzyCPLz8/9xO3PmzEH37t3h7u6OkJAQTJ48uc55a8Nql1ttAu5Wlj1Hll1UITsCERER1RLLnmOzurrKjtAoFIsFqU8+hYrERLvej2ubNmj5ztt1/rri4mL06NEDd999N2644YZ/XJ6QkIBLL70U99xzD15++WX4+vri4MGDcHd3BwCkpaUhLS0N77zzDjp37ozk5GRMmjQJaWlp+PHHH6tvZ8aMGXj33Xfx9ttvY8CAASguLsbx48fr/XgvxLhlj+fsObSconLZEYiIiKiWWPYcm5mmcVYkJqLs0CHZMc5p5MiRGDly5Hkv/+9//4trrrkGb731VvXn2rZtW/33rl274qeffqpx2bRp03DnnXfCZrPBarXi9OnTeO655/D777/jyiuvrL5u9+7dG/nRVDFkY9J0wWmcDi6HI3tERESGUVpYKDsCXYCLmxuE4A7GMum6jiVLlqBDhw4YMWIEQkJCMGDAgHNO9fyr/Px8+Pr6wmqtGmNbuXIldF1HamoqOnXqhPDwcNx8881ISUmxS25Dlj0hBDdVd3CF5TbYNF12DCIiIroIIQTKWPYcmoubO4TO4yqZMjMzUVRUhOnTp+Pqq6/GihUrMHbsWNxwww1Yt27dOb8mOzsbr776Ku6///7qzyUmJkLXdbz++uuYOXMmfvzxR+Tm5mLYsGGoqGj8wRJDTuMU4DROIygssyHAyxxzzImIiMyqorQEQrBIODI3L2+O7Emmnynb119/PaZOnQoA6NmzJzZv3ozZs2djyJAhNa5fUFCAUaNGoXPnznjppZdq3E5lZSU++OADDB8+HACwYMEChIaG4s8//8SIESMaNbchG5MQgJebIXuqU+F5e0RERI6PUzgdn7u3t+wITq9Zs2awWq3o3Llzjc936tSpejXOswoLC3H11VfDx8cHP//8M1xcXKova9GiBQDUuJ3g4GA0a9bsH7fTGAxZ9lQFCOSIkcNLzy+THYGIiIguoig3W3YEugh3L2/uXyyZq6sr+vXrhyNHjtT4/NGjR9GqVavqjwsKCjB8+HC4urrit99+q16p86xBgwYBQI3byc3NRXZ2do3baSyGHB6zWlQE+5hnVSKzyiwqhxCCL05EREQOSgiB4rzTsmPQRbh7+0C1cL0KeysqKsKxY8eqP05KSsLevXsRGBiIyMhIPPXUU7jlllswePBgDB06FMuWLcPvv/+OtWvXAvj/oldSUoJvv/0WBQUFKCgoAFA1emexWNChQwdcf/31ePTRR/HZZ5/B19cXzz77LDp27IihQ4c2+mMyZNkDgBCWPYeXU1QBAYBVj4iIyHGVnGPDZ3IsHr6+siM0Gtc2bRz2Pnbu3FmjcD3++OMAgIkTJ2LOnDkYO3YsZs+ejTfeeAOPPPIIoqOj8dNPP+HSSy8FAOzevRvbtm0DALRr167GbSclJaF169YAgK+//hpTp07FqFGjoKoqhgwZgmXLltWY7tlYDFv2grxY9hwdz9kjIiJycEKgtJBlz9G5e/nIjtAohKbVa7Pz+t6XUsfR0Msvv/yiC+HcfffduPvuu+v99QDg6+uLL7/8El9++WWd8tWHIc/ZAwB/z8ZvvtS4cooroHIKJxERkeNSFE7jNAB3b3OUvbqWL6PclyMzbNmzWlT4uht2YNIppOeXyo5AREREF6AoCvIy0mXHoAtQVBVunp6yY5BBGbbsAUCQN6dyOrITuSWyIxAREdFFnM5Ikx2BLsAso3okh6HLHrdfcGxpeWXQdG4ASkRE5Kg0TUNRTo7sGHQBHj7mWZyFmp6hy14zb5Y9R6bpArnFXKSFiIjIURWfzoUQuuwYdAFmWomTmp5hy54QgityGkBCVrHsCERERHQeOSeTZUegi/D08ZMdgQzMsGXPpgsEcWTP4SVkFtVqCVoiIiJqWkII5JxMkR2DLsI7MBBC5+gr1Y9hy54QPGfPCJK5SAsREZHDyuPiLA7PO7AZdF2THYMMyrBlz6IqaMbVOB3eidwSKNxrj4iIyOEoisKVOA3AOzAIimLYQ3aSzLDPHIuqINiHZc/RncjhyB4REZGj4h57js+3WQhUbhBO9WTYsgcAISx7Do977RERETkmTdNQmJ0tOwZdhE+zYNkRGo3ehFty1fW+Zs2ahe7du8PX1xe+vr6IiYnB0qVLqy8vKyvD5MmTERQUBG9vb4wbNw6nTp2qcRsnTpzAqFGj4OnpiZCQEDz11FOw2Ww1rrN27Vr07t0bbm5uaNeuHebMmVPvx1gbVrveup3xnD3HV1RuQ2FZJXzcXWRHISIior/gtgvG4O0fIDtCo1FVBSv/dxC56fZdrT2whReG3d2lTl8THh6O6dOno3379hBCYO7cubj++uuxZ88edOnSBVOnTsWSJUvwww8/wM/PD1OmTMENN9yATZs2Aah682TUqFEIDQ3F5s2bkZ6ejgkTJsDFxQWvv/46ACApKQmjRo3CpEmTMG/ePKxevRr33nsvWrRogREjRjT69wEweNnz9XCBVVVg48bdDu1ETgm6tOSywURERI5CCIHsE8dlx6CLcPf2gcXFXG+Y56YXIzulSHaMfxg9enSNj6dNm4ZZs2Zh69atCA8Px5dffon58+fjiiuuAAB89dVX6NSpE7Zu3YqBAwdixYoVOHToEFatWoXmzZujZ8+eePXVV/H000/jpZdegqurK2bPno2oqCi8++67AIBOnTph48aNeO+99+xW9gw9jVNVFLQM8JAdgy7iUHoBt18gIiJyMLlpqbIj0EV4BwbJjuCUNE3DwoULUVxcjJiYGOzatQuVlZW46qqrqq/TsWNHREZGYsuWLQCALVu2oFu3bmjevHn1dUaMGIGCggIcPHiw+jp/vY2z1zl7G/Zg6LIHAJGBnrIj0EXEpRfIjkBERER/k3eKK3E6Op/AZrIjOJXY2Fh4e3vDzc0NkyZNws8//4zOnTsjIyMDrq6u8Pf3r3H95s2bIyMjAwCQkZFRo+idvfzsZRe6TkFBAUpLS+3ymAw9jVMXAq0CPbFBdhC6oLj0Qm6/QERE5EAURUFeOsueo/MODIIQgsdRTSQ6Ohp79+5Ffn4+fvzxR0ycOBHr1q2THatBDF32bJpABEf2HN4hjuwRERE5nNPcdsHh+QQFQdc0WKyGPmQ3DFdXV7Rr1w4A0KdPH+zYsQPvv/8+brnlFlRUVCAvL6/G6N6pU6cQGhoKAAgNDcX27dtr3N7Z1Tr/ep2/r+B56tQp+Pr6wsPDPqemGXoap0VVEBnEsufo8ksrkVtcLjsGERERnWGrrERhdpbsGHQR3pzGKZWu6ygvL0efPn3g4uKC1atXV1925MgRnDhxAjExMQCAmJgYxMbGIjMzs/o6K1euhK+vLzp37lx9nb/extnrnL0NezD02wQWVUHbYG/ZMagW9p/Mw5AOIZyGQEREJJkQAqcSj3HbBQMIaNGSo3pN5Nlnn8XIkSMRGRmJwsJCzJ8/H2vXrsXy5cvh5+eHe+65B48//jgCAwPh6+uLhx9+GDExMRg4cCAAYPjw4ejcuTPGjx+Pt956CxkZGXjuuecwefJkuLlV7Q0+adIkfPTRR/j3v/+Nu+++G2vWrMH333+PJUuW2O1xGf7ZE87VOA0hNrUAQzqEyI5BREREANKOHJIdgWohMKyl7AiNLrCFl0PeR2ZmJiZMmID09HT4+fmhe/fuWL58OYYNGwYAeO+996CqKsaNG4fy8nKMGDECn3zySfXXWywWLF68GA8++CBiYmLg5eWFiRMn4pVXXqm+TlRUFJYsWYKpU6fi/fffR3h4OL744gu7bbsAAIowwZr4vV9didziCtkx6AJGdg3FrDv7yI5BREREAH6b8Qbit22SHYMuwOrmhke//kl2jDorKytDUlISoqKi4O7uXuMyXRdQ1aaZ5dWU99XULvQ9/jtDn7N3FrdfcHzcfoGIiMhxZBw7IjsCXYR/SKjsCI2uKcuXWYteXbHsUZNIzi1BuU2THYOIiMjplRUXoTAnW3YMugj/FmGyI5AJGL7sVWo6V+Q0ACGAIxmFsmMQERE5NSEEUg8flB2DaiEgNAy6xjfKqWEMX/YAoBVH9gxh/8l8mOAUUSIiIkNLO3pYdgSqBf/QMB43UYMZvuy5WFS0bmb/VX2o4XafOM2tF4iIiCRSFAUZx47KjkG1EMhtF6gRGL7sAUBUEMueEew4nis7AhERkVMTQiAjIV52DKqFABNuu0BNzxRlr5mPG9yspngoppaSW4r8Em6RQUREJEthTjYqSktkx6CLsLq6wcs/QHYMMgHTNKTwAJ63ZwSbE7I5/5yIiEgCIQRSDsXKjkG14NfcfNsukBymKXtRzVj2jGBr0mnZEYiIiJxW+tE42RGoFgK47QI1ElOUPZumo2MLX9kxqBZ2Hs/lIi1EREQSKIqCdC7OYgjNIlpBs9lkx6A6mDNnDvz9/WXH+AdTlD1FAbqw7BnC4YxClFVyzxgiIqKmpmk2ZJ84LjsG1UJwqyhTvjmu67rD31dKSgruvvtuhIWFwdXVFa1atcKjjz6KnJyc6uu0bt0aM2fObKSk9mWK9Vwtqopu4X6yY1AtaLrAruRcXNK2mSlfxIiIiByREAKZxxO5SbdBhLRuC9VikR2j0amqiiUfvoPc1BS73k9gywiMevjJOn9dYmIiYmJi0KFDByxYsABRUVE4ePAgnnrqKSxduhRbt25FYGCgHRKfX2VlJVxcXOr99aYoewAQ5u8BT1cLSir4IubotiZWlT0iIiJqOmmHD8mOQLVgdXWDX0hz2THsJjc1BZlJCbJjnNPkyZPh6uqKFStWwMPDAwAQGRmJXr16oW3btvjvf/+LuLg4JCcnY+rUqZg6dSoA1Fh8cPny5XjssceQkpKCSy+9FF999RVatGhRffkXX3yBd999F0lJSWjdujUeeeQRPPTQQwCA48ePIyoqCgsXLsQnn3yCbdu2Yfbs2fjXv/5V78dkmrKnKgo6hvpg94k82VHoInbwvD0iIqImpSgKkmP3yo5BtRAUHsHjJAlyc3OxfPlyTJs2rbronRUaGoo77rgD3333HeLj49GzZ0/cf//9uO+++2pcr6SkBO+88w6++eYbqKqKO++8E08++STmzZsHAJg3bx5eeOEFfPTRR+jVqxf27NmD++67D15eXpg4cWL17TzzzDN499130atXL7i7uzfocZmm7OlCoHMLX5Y9A9ibkgdN12FRTXHKKBERkcPTNBtSDnLbBSMIjoyCEIKFr4nFx8dDCIFOnTqd8/JOnTrh9OnT0DQNFosFPj4+CA2tuUVGZWUlZs+ejbZt2wIApkyZgldeeaX68hdffBHvvvsubrjhBgBAVFQUDh06hE8//bRG2Xvssceqr9NQpil7mi7QiYu0GEJZpY4DqfnoHu7PFzIiIiI7E0Lg5KEDsFWUy45CtdAsshV0TYPFaprDdENpyH7Qnp6e1UUPAFq0aIHMzEwAQHFxMRISEnDPPffUGBG02Wzw86u59kjfvn3rneHvTPMscrGo6NqSi7QYxerDmege7i87BhERkVNI3L1ddgSqpZDWbUy5OIuja9euHRRFQVxcHMaOHfuPy+Pi4hAQEIDg4ODz3sbfF1JRFKW6PBYVFQEAPv/8cwwYMKDG9Sx/+/f28vKq12M4F1PNo4sO9YHKgSJD+PNwFkf1iIiImoCiKEjas0t2DKql4FZteIwkQVBQEIYNG4ZPPvkEpaWlNS7LyMjAvHnzcMstt0BRFLi6ukKr48q2zZs3R1hYGBITE9GuXbsaf6KiohrzodRgqrLn7mJBq6DGa8JkPwfS8lFYVik7BhERkekV5+XhdHqq7BhUC55+/nD39pYdw2l99NFHKC8vx4gRI7B+/XqkpKRg2bJlGDZsGFq2bIlp06YBqNpnb/369UhNTUV2dnatb//ll1/GG2+8gQ8++ABHjx5FbGwsvvrqK8yYMcNeD8k80zjP6tTCB0nZxbJj0EUIAayMO4WxPVvy3SsiIiI7EULg6LZNsmNQLQVHtpYdwe4CW0Y47H20b98eO3fuxIsvvoibb74Zubm5CA0NxZgxY/Diiy9W77H3yiuv4IEHHkDbtm1RXl5e6/P87r33Xnh6euLtt9/GU089BS8vL3Tr1g2PPfZYvfLWhiIachaig6nUdMxel4B3VxyVHYVqYXT3Fvjw9t6yYxAREZnaoukvIWnPTtkxqBb6jr4Bl93+L6gGXrG8rKwMSUlJiIqK+se2AbquN9lja8r7amoX+h7/nam+AxZFQZcwrshpFOvjs6HrpnmvgYiIyOHo3HLBUELbdqia/mRSTVm+zFr06spU3wVVVdCtpb/sGFRL+aWVOJCa36AlbomIiOjchBBIPXyIWy4YSMvoTlyJkxqVqcoeAAT7uMHPw+XiVySHsPzQKdkRiIiITOvYzq2yI1Atefr5wzswSHYMMhnTlT0A6MzN1Q1j7ZFMLtBCRERkB9xywVhatOsgOwKZkOnKnqYLdA/n5upGcSi9AHklnF5CRETU2Apzc7jlgoGEtusAzWaTHYNMxnRlDwD6tQ6UHYFqSQhg5aFMnrdHRETUiIQQOLZ9i+wYVAdhHcx1vp6u67IjmFZdjptNt8+eRVXQPyoQimLqxYxMZfnBDNzU1/57rhARETkLRVGQtJfbLRiGoiC0bQdTnNri6uoKVVWRlpaG4OBguLq6muJxOQohBLKysqAoClxcLr5OienKHgD4erigbbA3jmUWyY5CtbD+aDZKK2zwcDXl05GIiKjJaTZuuWAkgS1awtXDQ3aMRqGqKqKiopCeno60tDTZcUxJURSEh4fDUouRYFMeXetCoF/rAJY9g6jQdCyJTce43uF854eIiKiBhBBI2ruLWy4YSKjJFmdxdXVFZGQkbDYbNE2THcd0XFxcalX0ALOWPV2gb+tALNieIjsK1dLifem4sQ+nchIRETWUoig4tG617BhUBy3OLM5isZrn0PzsNMPaTDUk+zHlAi1Wi4qYNtynxEg2HstGcQVXoCIiImqoyvIyJO7ZITsG1UFYdCdTFT1yHKYsewAQ5u+B5r5usmNQLdl0gd/2pnFVTiIiogYQQiBuw1polZWyo1AtWd3c0CyitewYZFKmLXsAt2AwmsX703jOHhERUQMoioJDG/+UHYPqoKXJtlwgx2Laslep6ejLsmcoWxNzUVjKdyKJiIjqqzjvNFIPH5Idg+ogvHNXbqZOdmPasufC8/YMR9MFfuFUTiIionoRQiB2zQpuNGwwkV17cmSP7Ma0ZQ8A2od4w8eNJ7saCadyEhER1Y+iKIjjFE5Dsbq5IbRtex77kN2YuuypqoLerQJkx6A62HE8F6eLuS8QERFRXQghkJ2SjNzUk7KjUB2EdejIUT2yK1OXPZumo29rlj0j0QXw4+5UTuUkIiKqo9g1y2VHoDoK79QNusbz9ch+TF32LKqCAVE8b89oFm5P4XQGIiKiOhBC4PCm9bJjUB216tYDisqRPbIfU5c9RVHQM8IfLhYWByNJyCrC3hN5HN0jIiKqBSEETh6MRUl+nuwoVAdWVzeEtu3AN7jJrkxd9gDA1aqiW0s/2TGojr7eepwvfkRERLWgKApi166UHYPqiOfrUVMwfdmz6Touax8sOwbV0ZL96Sit0GTHICIicni2ygoc27FFdgyqo/BOXXm+Htmd6cueRVFwZacQ2TGojsptOr7bmcKpnERERBcghMCRLRthK+dK1kYTyfP1qAmYvuwpioKuLf0Q6OUqOwrV0cLtJziVk4iI6AIURUHcBu6tZzSuHh5o0S6axzlkd6YvewCgKgoua99Mdgyqo8MZhTiUWsDRPSIionMQQqAgKxPJsXtlR6E6iuzWk+frUZNwirJn03QM7cipnEY0d+tx2RGIiIgc1o7fFwF8U9Rwonr2hWbj+Xpkf05R9qwWFVdEh0DlSLnh/L4vDeU2XXYMIiIih2MrL8fBtatkx6B6aNunPyxWq+wY5AScouwBgK+HC7qH+8uOQXVUUqFh0e6TnMpJRET0F0II7F2xBJXlZbKjUB01i2wNL/8A2THISThN2bNpOoZGcwsGI5q3jQu1EBER/ZUQAnuWLZYdg+ohqmcf6Dq3l6Km4TRlz6IquLJTc9kxqB4OphVgx/Ecju4RERGhqugd27EFhTlZsqNQPbTp0x8K+CY2NQ2nKXtnt2AI4hYMhvTJn4kc3SMiIkLVMc3O3xfJjkH14OrhgbD2HaGoTnMITpI53TNtcAdO5TSitUczcSK3mKN7RETk1IQQyEpOQnr8EdlRqB645QI1NacqezZNxxUdWfaMSAjg4z8TZMcgIiKSSlEUbPv5e9kxqJ7a9OKWC9S0nKrsWS0qLucWDIb1y55UFJbxBZKIiJxXSUE+jm7bJDsG1VOb3txygZqWU5U9APBxd0HPCC53a0TlNh2fbUjkVE4iInJKQgjs+O0nCJ37zxpRcKsobrlATc7pyp5N03E5t2AwrG+3JqOSv+SIiMgJabZKxK5ZLjsG1VP7/pdA17jlAjUtpyt7FlXBVdyCwbDySiqxcHsKR/eIiMipCCEQu3oFyouLZUeheoq+ZDBX4aQm53TPOEVR0DnMFy383GVHoXr6cmMSWPWIiMjZ7F76m+wIVE+BYeEIDGvJbaSoyTld2QMATRe4plsL2TGonpJzSrDy4CmO7hERkVMQQiBxzw7kZaTJjkL11K5/DKdwkhROWfYUBRjTq6XsGNQAn6w9xnfHiIjIKSiKgh2//iQ7BjVA1RROHrdQ03PKsqcqCrq19ENEoIfsKFRP+07mY+3hTI7uERGRqQkhcHzfbqQePig7CtWTb3BzhLSKgqI45WE3Sea0zzpNF7i2W5jsGNQAb684wtE9IiIyNUVRsGHBXNkxqAHa94+BzpXESRKnLXtVUzlZ9ozsYFoBVh/iuXtERGROQgjE79iCzKQE2VGoAaJjLgPfmiZZnLbsqYqC6FBfRDXzkh2FGuCt5Ue4MicREZmTENjIUT1D8woIRIv20dxygaRx6meepgtc252rchrZkVOFWHYgnaN7RERkKkIIHFr/J3JTT8qOQg3Qrt9ACE7hJImcuuypCjCmJ1flNLp3VxwFux4REZmJEAKbfvhWdgxqoOiBl3IGEknl1GVPURS0DfFG+xBv2VGoARKyivHL3lSO7hERkSkIIbB3+WIUZmfJjkIN4BUQiPBOXaFyCidJ5PTPPpuu49oeXKjF6N5fHQ+dXY+IiExAt9mw7efvZcegBuo4aAhH9Ug6py97FkXB2J4se0aXnFOCH3elcHSPiIgMTQiBHb8vQkl+nuwo1EBdhlzJLaJIOqcve4qiIDLIC51b+MqOQg30wepj0Di8R0REBlZZUY6dixfJjkENFBTRCsGRrVn2SDqnL3sAYNN0rsppAql5pZiz+ThH94iIyJCEENi26DuUFxfLjkIN1PnSy6FrmuwYRCx7AGC1qBjbi6tymsEHq+NRVGZj4SMiIkMRQqC8uAi7l/4mOwo1lKKg85AroVosspMQseyd1cLfAz3C/WTHoAYqKLNh+rLDnDZBRESGs+n7ebCVl8uOQQ0U0bkrvAMCZccgAsCyV82m6RjD0T1TWLD9BBKziji6R0REhiCEQHHeacSuXiY7CjWCTpcNhWazyY5BBIBlr5rVouLGPuFws/JbYnS6AP778wGO7hERkSEoioJ13/6PBcEErC6uiI65DBarVXYUIgAsezX4uLvg6q6hsmNQI9iSmINVhzI4ukdERA5NCIGUuAM4vHGt7CjUCNr06Q9Xdw/ZMYiqsez9haYL3DGglewY1EheXnyIWzEQERnE+vXrMXr0aISFhUFRFPzyyy8XvP7GjRsxaNAgBAUFwcPDAx07dsR7771X4zrz5s1DREQEAgIC8Pjjj9e47Pjx4+jQoQMKCgoa+6HUidB1rPrsI6kZqPF0GXIlV+Ekh8Ky9xcWVUH/qEBENfOSHYUaQUpuKT7fkMjRPSIiAyguLkaPHj3w8ccf1+r6Xl5emDJlCtavX4+4uDg899xzeO655/DZZ58BALKzs3HvvffinXfewYoVK/Dtt99i8eLF1V//0EMPYfr06fD1lbfPrhAC2375AblpJ6VloMbj5R+A1j37cBVOciicUPw3Nk3HLf0iMH3pYdlRqBF8tOYYbusfCT8PF57DR0TkwEaOHImRI0fW+vq9evVCr169qj9u3bo1Fi1ahA0bNuD+++9HYmIi/Pz8cMsttwAAhg4diri4OFx77bVYsGABXFxccMMNNzT646gtIQSKcnOx/efvpWWgxtXl8qtkRyD6B47s/Y3VouLWfhFwsbAYmEFxhYZpf8Sx6BERmdyePXuwefNmDBkyBADQvn17lJSUYM+ePcjNzcWOHTvQvXt3nD59Gs8//zw++kju1ElFUbDi0/dhq6yQmoMaiaKg5/BreLxBDodl7xz8PV0xrHNz2TGokfy46yQOpOZxOicRkQmFh4fDzc0Nffv2xeTJk3HvvfcCAAICAjB37lxMmDAB/fv3x4QJEzBixAg8+eSTmDJlCpKSktCrVy907doVP/74Y5NmFkIgfvsWHN+3u0nvl+ynVfde8AkKZtkjh8NpnOdg03TcMaAV/ojNkB2FGoEQwJM/7MeSRy4DB2yJiMxlw4YNKCoqwtatW/HMM8+gXbt2uO222wAAY8eOxdixY6uvu27dOuzfvx8ffvgh2rVrhwULFiA0NBT9+/fH4MGDERIS0iSZNZsNa/43u0nui5pGj6tGQtNssFh4aE2OhSN752C1qBjUrhnCA7h0rlkczijEJ2uPcXSPiMhkoqKi0K1bN9x3332YOnUqXnrppXNer7y8HA899BA+/fRTHDt2DDabDUOGDEF0dDQ6dOiAbdu2NUleIQQ2zP8KRadzmuT+yP68/APQtu8AFj1ySCx752HTqxZqIfP4aM0xpOWVsvAREZmUrusoLy8/52WvvfYarr76avTu3RuapsH2lw3MKysroTXBcvlCCJxOT8WeZYsvfmUyjK5Dh8mOQHRefAviPKyqitv6R2Lmqnju1WYS5TYdU7/fh+8fiJEdhYiI/qaoqAjHjh2r/jgpKQl79+5FYGAgIiMj8eyzzyI1NRVff/01AODjjz9GZGQkOnbsCKBqn7533nkHjzzyyD9u+9ChQ/juu++wZ88eAEDHjh2hqiq+/PJLhIaG4vDhw+jXr18TPEpg6cczIHS9Se6LmoCioMcwLsxCjotl7wKaebthaHQwVsVlyo5CjWR7Ui7mb0vGbf0j+cJMRORAdu7ciaFDh1Z/fHYT9IkTJ2LOnDlIT0/HiRMnqi/XdR3PPvsskpKSYLVa0bZtW7z55pt44IEHatyuEAL3338/ZsyYAS+vqn10PTw8MGfOHEyePBnl5eX46KOP0LJlS7s+PiEE9q1aioxjR+16P9S0WnfvBZ+gZrJjEJ2XIjin7bxsmo718dm4e84O2VGoEfm4WbH+6aHw5957RETUBIQQKC8pxhcP34Py4mLZcagRXffEf9GmTz+er0cOi+fsXYDVouLyDsEI9XWXHYUaUWG5DU//uJ9Fj4iImoSiKFjzv9kseibjFRDIhVnI4bHsXYQAcHO/cNkxqJGtOHQKyw6kc7EWIiI7W79+PUaPHo2wsDAoioJffvnlgtffuHEjBg0ahKCgIHh4eKBjx4547733alxn3rx5iIiIQEBAQPV0z7OOHz+ODh06oKCgoLEfSr0IIZByKBZxG9fKjkKNrPuVI1B1pEjkuFj2LkJVgAkDW8OFG7SZzvO/HkRppcbCR0RkR8XFxejRowc+/vjjWl3fy8sLU6ZMwfr16xEXF4fnnnsOzz33HD777DMAQHZ2Nu6991688847WLFiBb799lssXvz/q1s+9NBDmD59Onx9fe3yeOpCCAFbRQWWffLexa9MhmKxWtHr6tFQVYvsKEQXxHHni1AUBc183HBt9zD8vCdVdhxqRFmF5Xjxt4N4+8YesqMQEZnWyJEjMXLkyFpfv1evXujVq1f1x61bt8aiRYuwYcMG3H///UhMTISfnx9uueUWAMDQoUMRFxeHa6+9FgsWLICLiwtuuOGGRn8c9aEoClZ98TEKsrjQm9l0HDQEHj7y31AguhiO7NWCpgtMGtJWdgyygx92nsSKgxkc3SMiclB79uzB5s2bMWTIEABA+/btUVJSgj179iA3Nxc7duxA9+7dcfr0aTz//PP46KOPJCeuIoRA/PYtOLR+jewoZAd9R98AnVtokAGw7NWCRVUQHeqDQe2CZEchO/j3T/uRV1LJwkdE5EDCw8Ph5uaGvn37YvLkybj33nsBAAEBAZg7dy4mTJiA/v37Y8KECRgxYgSefPJJTJkyBUlJSejVqxe6du2KH3/8UUp2IQTKiouw8rMPpdw/2VdEl25oFtEKqsrDaHJ8nMZZSzZNx6QhbbHp2P+1d9/hUVVrG4d/e8+k9wRIIEBCCyC99yYdRAUsIIioKAjIAcWjeLB8ih1RQTmioiiKggoqoAexISDFBkiX3nsnIcnM7O+PIQhKJ5k9mTz3dWHIzGTvN5jM7GfWWu/ab3cpkssOpWczYNJvTLqrvt2liIjISXPnzuXYsWMsXLiQhx56iLJly9K9e3cAOnfuTOfOnU89ds6cOSxbtowxY8ZQtmxZPvzwQ5KSkqhbty5NmzalSJEiPq3dMAy+HDOSjKP+0SRGcletazrjcbsxHVqvJ/5Pb0lcJKfDpEm5wqQlRtpdiuSBn9bvZ9yc9RrdExHxE6VKlaJKlSrcddddDBkyhMcff/ysj8vMzKR///6MGzeOdevW4XK5aNasGeXLlyctLY1Fixb5tG7Lslgyayablvzq0/OKb8QmFaN0jToKepJvKOxdApfbw91NSttdhuSRF79ey/q9xxT4RET8jMfjITMz86z3jRgxgnbt2lGzZk3cbjcul+vUfdnZ2bjdbl+ViWVZGIaBKztbYSBA1WzfCUtr9SQf0TTOS+B0mFxfI5nnZ61hz9Gzv+hI/pXl9tB34m98NbgxQaapTddFRHLBsWPHWLdu3anPN27cyJIlS4iPj6dkyZIMGzaM7du389577wHw2muvUbJkSSpUqAB49+kbOXIkgwYN+sexV65cyeTJk/n9998BqFChAqZpMn78eJKSkli9ejV16tTxwXfpZXksdm08TK0O11KiUhVmvPwch3bt8Nn5JW+FhEdQ+eo2CvKSryjsXSLDMOjdMJXnZ62xuxTJA+v3HuP/vljJU52r2F2KiEhA+OWXX2jRosWpz3M2Qb/tttuYMGECO3fuZMuWLafu93g8DBs2jI0bN+J0OilTpgzPPfccffv2PeO4lmVx9913M2rUKCIiIgAICwtjwoQJDBgwgMzMTF599VWSk5N98F16/TR1PUu/3Ur5+kk0v6UcvV4Yw3fjX2f5D7N9VoPknSpXt8HpDLK7DJFLYlias3bJjp1wUffpb0jP8t3UEPGtt2+rTYsKRTS6JyIiF2RZFltXHWD6mKVw8qoqLCqIzvfXJC4pgrWL5vP1uNFkHj9ub6Fy2QzT5O7X3iEiLl7XBpKvaM3eZQgPcXBT7RJ2lyF5aOgnyzicoe0YRETk/CzLIjM9m2/eWXkq6AFkHM1m0uOL+H32ZsrWrk/vF/9L8YqV7StUrkj5+o2JjE9Q0JN8R2HvMt3dtDQOU7/wgerA8SwGTPoNZT0RETkvC/43bjkZR7PPevdPn67n0xd+wxkcyU2PPUPjbr205iu/MQwa3HCLNlGXfElh7zKYhkGx2DDaVkq0uxTJQ/PX7ecFrc0UEZHzWPj5BravPXTex+zZdJR3HpjHlpUHqHvdDdzy1Chik4r5pkC5YmVr1SM+ubg2UZd8ST+1l8nt8dC/eVm7y5A89t8565m9cpemc4qIyBksy2Lziv389vXmi3q8xwMzxizl2/dWk5Bckl7Pj6Fy89Z5XKXkhvo3dMfjUZ8GyZ8U9i6TwzSpnBxDndQ4u0uRPDZk8lK2HcxQ4BMREcAb9NKPZDF7/Ioz1uldjDULd/HefxZy7KCLtvf8i05DHiLkZDdR8T8p1WqSWKoMpqmpt5I/KexdAZfbQ79mZewuQ/LYsUwXvd9ZTJbLo8AnIgXKM888Q506dYiKiqJIkSJcf/31rFlz/untK1asoGvXrqSmpmIYBi+//PI/HvPBBx9QokQJ4uLiTm3FkGPTpk2kpaVx5MiR3PxWco1lWXg8FjNeXUpmuuvCX3AWZzRvqdNAzVv8WIOu3fC4Naon+ZfC3hVwOkxaVkykSnKM3aVIHlu/9ziDPlqiLlwiUqDMmTOHAQMGsHDhQmbPnk12djZt2rTh+Hm2EEhPT6d06dI8++yzJCUl/eP+ffv20adPH0aOHMnXX3/N+++/z4wZM07d379/f5599lmio6Pz5Hu6UoZh8P3E1ezbeuyKj6XmLf4tuWIlkstfpf8nkq8p7F0hl9vD0DZpdpchPjBrxS7Gfr9Oo3siUmD873//o3fv3lSqVIlq1aoxYcIEtmzZwq+//nrOr6lTpw4vvPAC3bp1IyQk5B/3b9iwgZiYGG6++Wbq1KlDixYtWLVqFQAffvghQUFBdOnSJc++pythWRZ/zNnGmoW7cu2YOc1btuY0bxnxIrGJRXPt+HL5GnTprlE9yfcU9q6Q02HSrHwRapaMtbsU8YGRX69hwYb9CnwiUiAdPnwYgPj4+Ms+Rrly5UhPT+f333/nwIED/Pzzz1StWpWDBw/yyCOP8Oqrr+ZWubnKsix2bzrCvCl/5vqxPR6YntO8pXgKvV54lUrNW+X6eeTiJZYpR0rV6hrVk3xPYS8XuNweHmhb3u4yxAc8Fgz44Df2Hs1U4BORAsXj8TB48GAaNWpE5cqXv74sLi6Od999l169elG3bl169epF27ZtGTp0KAMHDmTjxo3UqFGDypUr88knn+Tid3D5chqyfDl2GR533j33n2recshFu3sGq3mLjep3vhmP+/LWZIr4E6fdBQQCp8OkQZlC1CsVz6KNB+wuR/LYwfRs7nj3Z6bd0winA63jE5ECYcCAASxfvpx58+Zd8bE6d+5M586dT30+Z84cli1bxpgxYyhbtiwffvghSUlJ1K1bl6ZNm1KkSJErPuflsiwLt8vD9NFLzrlxem7KOJrNpMcW0bBrGapd3YBiaRWZOfoFtq1anufnFq+EEimUrVPf7jJEcoVG9nKJRvcKluXbj3DvR79fasdtEZF8aeDAgcyYMYPvv/+e4sWL5+qxMzMz6d+/P+PGjWPdunW4XC6aNWtG+fLlSUtLY9GiRbl6vktlGAZzJ//J/u3nbkqTF376dD1TX/gNZ0gUNz32DI1uvlVTCn2k4Y23aK2eBAyFvVzidJjUTo2ncdlCdpciPvK/5bt4auYqu8sQEckzlmUxcOBApk2bxnfffUepUqVy/RwjRoygXbt21KxZE7fbjcv119S57Oxs3DZfdLtdbupdW5rCJaN8fu7dOc1bVh2g3vU3qnmLDxQpVYa0eo0UrCVgKOzlIpfHw7/baXSvIBk/byPvLdik9XsiEpAGDBjA+++/z6RJk4iKimLXrl3s2rWLjIyMU4/p1asXw4YNO/V5VlYWS5YsYcmSJWRlZbF9+3aWLFnCunXr/nH8lStXMnnyZJ544gkAKlSogGmajB8/npkzZ7J69Wrq1KmT99/oWViWxaFPPmFTly4Em9l0eaAWZWoW9nkdHg9MH72UbyeqeYsvNLmlt0b1JKAYlq5Sc93t7/zM92v22F2G+IhpwNu969AsrbDW74lIQDnXc9o777xD7969AWjevDmpqalMmDAB8G6KfrYRwGbNmvHDDz+c+tyyLJo0acJDDz3ENddcc+r2GTNmMGDAADIzMxkxYgR9+vTJte/nYlmWRfqvv7Ll9jsgOxszOppS06YRnFyMhZ+v59evNvu8JoDw6GA631+D2MQI1i6cx9dvjCHzPHseyqUpXrEyNz/+rN1liOQqhb1c5vZYrNl1lA6j59pdivhQWJCDqf0bUiEpSoFPRCQfsyyL7G3b2Nj1BjxHjpxxX8l3JxBRrx5rFu3iu4mr8LjsuYRqdGNZqjZPJv3wITVvyUW3jHiRxNJlNYVTAoqmceYyh2lwVbFo2lZKtLsU8aGMbDe9xi9m7zFtySAikl9ZloX78GG29L79H0EPYMttvTnw3kTS6iTS+b6ahEUF2VAlzP94nZq35LJSNWpTtFx5/TtKwNHIXh5wezxs3Hec1i/9iP51C5YyhSOZPrARYcEOjfCJiOQjlmVhnTjBpu63kLl69XkfG9O1K4n/939kHMlm+pilHNhpz1RK04SOA6tRomI8uzesY+Yrz3No905basnXDIPbXniV+OTimKbCngQWjezlAYdpUrZIFB2rqGNWQbN+7zFun/AzHo+lET4RkXzCsixwu9nar98Fgx7A4U8/ZUuPHoSFwQ0P1abkVfE+qPKfTjVveW8VhUqkepu3NGtpSy35WfkGTShUIkVBTwKSRvbyiNtjseVAOi1f/AGP/oULnGurFWN09xp2lyEiIhfB8njYfv9Qjn711SV9naNwIUpNnYYzIYF5H//Jsu+35VGFF6bmLZfHdDi44+U3iCpUGNPUGIgEHv1U5xGHaVCqUATX10i2uxSxwRdLd/DE9BV2lyEiIhdh1xNPXnLQA3Dv3ce6Fi04sWI5TW5Oo9ktaZimPVP4049k8cFji1jy7RbK1mlA75FjSa5YyZZa8pNKzVoSUyRRQU8Clkb28pDHY7HvWCZNX/ieE9keu8sRG9x7dVnub6O9F0VE/NWel19h/+uvX/Fxij7zDDHXXcf2tQf5atxysjJcF/6iPJKYGsU191YlJDyYRdOmsOCTSdo77iycQcHcOeYtImJiMRT2JEDpJzsPmaZBocgQ+jYtY3cpYpMx361j3Jz1dpchIiJ/Y1kW+ye8mytBD2DnsGHsfu5ZipWL5cZhtYkpHJYrx70cuzcd5Z0H5rN11QHqdb6J7k+OJDZRfQT+rtY11yvoScDTyJ4PZLrcNH/hB3YePmF3KWKTZzpXplvdkurQKSLiByzL4vC0aez8z3Byu212eIMGFH/9dVxuky//u4wdfx7K1eNfqgoNitKse1ksy8O348eyYs63ttbjLyLi4rlz9JsEBYfYXYpIntJbGT7gMAyGdahodxlio/98tpwvluxQh04REZtZlsWR6TPYOfyRXA96AOkLFrChQwccWce5bnB1KjSwd0Rt9YKdTBy+iOOHXLTrP4RrBj9ESESErTX5g8Y398LhcNpdhkie08ieD3UZ+xO/bTlodxliE4dpMKZbddpXKaoRPhERG1iWxdGvZ7P9vvsgr9ewhYZS6pNPCC1bht++3szCaett33u38Y1lqdI8mfTDh5gx+nm2ryqYjcSKlCpDz2de1muxFAga2fMRl9vDE9dVQs8rBZfbYzHooyV8s3K3RvhERHzMsiwMw+DIV1/lfdADOHGCjddcw5HZ31CjdUna96tCUIi9+7jN+3gd00b+jjMkkpsfe5ZGN9+K6Sh4e8tdfXtfLI8a50nBoLDnI06HSeXkGLpoK4YCzeWx6D/pN35Ys1eBT0TERyzL4oetP7Dz6A6SXxxJVNs2Pjv39nvvZd+rr5FSOYGu/65FZJy9a8R2bTzibd6y+q/mLTGJSbbW5Evl6jUiufxVBTLkSsGkaZw+5PFYHEjPounz35OepRbIBVmww+StXrVoklZY00hERPKQZVl8t+U7hs4ZimmYfHHdFxSLSGL70Acua2+9yxXZuhXFXnyJrEwPM15dyp7NR3127nOp2KAoTU82b/nmrddY+eN3dpeUpxxBQdzx8htExidoXz0pMPST7kOmaRAXHkz/5mXtLkVsluX20Oe9X5m9QlM6RUTyimVZfLP5G4bOGYrLcpHlyeKaz69h23HvCF90xw4+q+XY7G/Y1Pl6goxsujxQi7K1ivjs3OeyKqd5y2EX7QfcxzWDHwzo5i01219LlIKeFDAa2bNBtstDixd/YNvBDLtLEZs5TIMXbqhK5xrJGuETEclFlmUxe9Ns/j3337itM2fTOE0nn1/7GSWiSrDjwYc4Mn26z+oyIyNJ/ewzQoons+iLDfzy5Safnft8Ar15S3hMLH1Gv0VQaKjdpYj4lN7asIFhwH+0FYPgbdpy/8dLmbhgs92liIgEDMuymLVp1lmDHoDL46LTZ9ey+chmij33LDHXX+ez2jzHjrGhVSuO/fQT9a4tTes7rsLhtP9ybN7H65j24hJv85ZHn6HRzT0Dal1bo5t74ggKsrsMEZ/TyJ6Nbh63gEUbD9hdhviJoW3KM/BqTfEVEblS09dP55H5j5w16J3OxGTqtVMpHVOKncMf4fDUqT6q0KvIQw8R16sXezcfYebYZWQczfbp+c/GNOGae6tTvEIcuzesY8Yrz3F49y67y7oiiaXL0uPplzSDRgokhT2buDwe1u05RodX5uLR/wE5qW/T0gzTqK+IyGV7Z/k7vPTrS1hc3Iuricmn135C2bhy7HzkUQ59/HEeV3im6M7Xk/TkCDKOuZg+egkHdhz36fnPpWLDojTtlv+btxiGSc9nX6ZQiZSAGqkUuVgKezZ78NNlTP55q91liB/pWa8kT1xfGQP0LqSIyEXI2UNv5C8jeXfFu5d1jE86fUL5+PLsfOxxDk2enMsVnl9olSqUeO89LEcQs95Yzubl+316/nMJjwmm8301iE2MYM2Cucx+41Uy0/0jjF6s6m2v4erb++r1VAoshT0beSyLw+nZNBv5PUcyXHaXI37kuurFGHVTdUxDgU9E5Hwsy8JjeRg+fzgzNsy4omNNuWYKFRMqsuuJJzk4aVIuVXhxzIQESk+bhrNQIeZ/uo6l3/rPG8E5zVuOHz7EzFeeZ/vq/NG8JSI2jjteeYOgkFC9lkqBpbBnM7fH4uNftvLQ1D/sLkX8TOurEvlvj5o4TEMvUiIiZ2FZFtnubP71w7+Yt31erhzzo44fUalQJXY99RQHJ76fK8e8aA4HqZMmEVatKivmbufHD9fi8ZO1HkmlY+g4oAohYU4WTpvCwk8/xOP27z2DO/7r36TVa6Tpm1KgKez5ie5vLGTBBv+YtiH+o26peN6+rTYRIU4FPhGR01iWRXp2OnfPvptl+5bl6rE/6PABVQtXZfczz3Lg3cubFnolkkY8SWyXruz48xBfjfuDzHT/mP2Tn5q3pFSpzg3DR9hdhojtFPb8gNtjseNQBq1GzSHT5bG7HPEzqQnhfNCnPsViNQ1FRAS8QW93+m7u+vouNh3ZlCfneK/9e9QoUoPdz7/AgbffzpNznE/crT0p8tAwjh44wfQxSzm8x3/25q3YqChNby6HZbn9snmLIyiI20f9l6hChTFNjepJwaaw5yc8HovX56zn+Vlr7C5F/FBseBBv965DjRKxCnwiUqBZlsWqA6voN7sfBzMP5um5JrSbQK3EWux58UX2v/lWnp7rbMLr16f46+NwWSZf/ncZO9Ye8nkN5xIeE0zn+2sSWyTc75q3NLihOw26dscw7d+/UMRuCnt+xO2x6DRmHit3HrG7FPFDwQ6T52+owvU1ittdioiILSzL4rst3/Hg3AfJdGf65Jzj24ynbtG67HnpZfaPG+eTc57OWawYqZ9+iiM6hh8mrWbV/J0+r+F8Gt9UjirNivlN85bYpGL0fnEsDqfT1jpE/IXCnh9xuT2s2X2Ua1+dj9tPFmSL/xl4dVmGtil/qtW4iEhBMWHFBEb9Muqi99DLLW+2fpP6xeqzd8wY9r021qfnBiA0lFJTphCaVo7fZ29hwdR1+NPVW1LpGDoOrEJIqP3NW24YPoISV1VRUxaRkxT2/IxlWTz15SremrvR7lLEj11brRijbqqmTp0iEvAsy8LC4plFz/DRmo9sq+P1Vq/TKLkRe18by74xY2ypIfnll4hq25bNy/fz9VsryM70n26Y/tC8pUKjZnQc9IBPzyni7xT2/FCmy02rUXPYesB/FmOL/6lZMo4Jt9cmKjRIgU9EApJlWWS4Mhg6Zyhzt8+1uxxea/kaTYs3Zd/rr7P35VdsqSHhnnsoNHAgh3anM33MUo4d9M101otVsVFRmnYrh+XxbfOW8JhY7nhpHMFhYVqrJ3IahT0/5HJ7WLzxALe8tcjuUsTPlYgP4/0761EyPlyBT0QCimVZbDmyhf7f9mfL0S12l3PK6BajaVGyBfveeJO9o0bZUkNky6sp9tIrZGV5mPnqMnZv8q+1/uExwXS5vyYxPmzecu39/6FMrbqavinyNwp7fuz+KUv49Lftdpchfi4yxMnIG6vSrnJRu0sREck1X2/6muHzh5Ph8r9ZLi83f5mWKS3ZP348e14YaUsNwaVLkzJ5MkZYON9MWMm6X/bYUsf5NLmpHJV90LylfIMmXDP4wTw5tkh+p7DnpzyWxfFMFy1G/sC+Y1l2lyP5wJ2NS/GfDhUxDDTKJyL5Us76vJd+fYkJKybYXc55jWw2krapbdk/4V32PPusLTWYERGkfjaNkBIlWDxjIz/P8L/1/nndvCUsOoY7Xh5HcFg4pqZvivyDwp4fc7k9fLV8F/d++LvdpUg+USc1jrd61SY6TOv4RCR/yVmfN/j7wSzYucDuci7Kc02fo0OpDhyY+D67n3rKtjpKvPUmkY0b8+fPu/n2vVW4sz221XI2ptPkmoFVKV4+95u3dLpvGGVr19f0TZFz0FsgfszpMOlUrRhXVyhidymST/y86SCtRv3I71sPofdxRP7y448/0qlTJ4oVK4ZhGHz22Wdn3G9ZFo8++ihFixYlLCyMVq1a8eeff573mEePHmXw4MGkpKQQFhZGw4YN+fnnn894zMiRIylSpAhFihThxRdfPOO+RYsWUatWLVwuV658j/maZWF5sjENk13pvu3geCUe/PFBpq+fTvytPUl89BGw6U22rX3uYv/bb1OmVhG63F+T8OhgW+o4F4/LwxcvL+H791dTqGQpbnvhNa5qevUVHzetfiPS6jVS0BM5D4U9P+f2WDzbpQqRIdocVC7O3mOZ3Pj6Asb9uAFAoU8EOH78ONWqVeO111476/3PP/88o0eP5vXXX2fRokVERETQtm1bTpw4cc5j9unTh9mzZzNx4kT++OMP2rRpQ6tWrdi+3bvWetmyZTz66KN89NFHfPjhhwwfPpw//vgDAJfLRb9+/Xj99ddxFuTNn3Oen34Zj/lKdZyWxZirxxDuDLe3rkvw8LyH+ezPz4jr3p2kxx6zLfDtef4Fdg57iITkCG56uA4JyRG21HE+q+bvZOLwhaQfcdN+wH10/Ne/CQm/vDrDoqJpdddALI9/jWKK+BtN48wH3B6Lqb9t44FPltldiuQzbSsl8Uq3aoQ4HZrWKXKSYRhMmzaN66+/HvC+IVKsWDHuv/9+hg4dCsDhw4dJTExkwoQJdOvW7R/HyMjIICoqis8//5yOHTueur1WrVq0b9+eESNGMGXKFEaNGsXChQsBqFevHkOHDuXGG2/kmWeeYdeuXbzyij3t+/2CZYE7Cz4fAH987L2tbCvct0zhmy3fMHTOUHvru0SPN3icLuW6cOiTT9n16KPYtet5aOXKlHjvPXAGM+vN5Wz6Y78tdVzIqeYthw56m7esWXlJX3/N4AcpV7ehRvVELkAje/mAwzS4sXYJOlRJsrsUyWdmrdhF+1fmsXHfMTx6X0fkrDZu3MiuXbto1arVqdtiYmKoV68eCxacfe2Yy+XC7XYTGhp6xu1hYWHMmzcPgCpVqrB27Vq2bNnC5s2bWbt2LZUrV2b9+vW88847jBgxIu++KX9neeDABhjX5K+gB7DuGxwLx9I2tS09K/a0r77L8PiCx/l47cfE3tCVok+N8O4yboMTy5ezvmVLrIP76dC/KtVblbCljguZO+VPpo1aQnBYNDc//iwNb+xx0fvjlavbkPINmijoiVwEhb18wuOxeL5rNYrGhF74wSKn2bjvOB1Gz+Ojxd59qjSYL3KmXbu8a8QSExPPuD0xMfHUfX8XFRVFgwYNePLJJ9mxYwdut5v333+fBQsWsHPnTgAqVqzI008/TevWrWnTpg3PPPMMFStWpG/fvjz//PPMmjWLypUrU6NGDX788ce8/Sb9Rc7zz+I34L8NYe+afz7m6+Gw43eG1h5KjSI1fFvfFXpy4ZN8tPojYjp3pugzT9sW+DwHD7KuRQsyliyh0Q3laHFrBUyH/83u2LX+MG//+ye2rT1E/a7duOXJkcQUSTzv14RFRdP6bk3fFLlYCnv5hGkahAaZvNKtBqb/PV+LnzuR7eHhacu5892fOZrhUuATyQUTJ07EsiySk5MJCQlh9OjRdO/e/Yz27/369WPNmjWsWbOGfv368e67754Kin369GHatGmMGjWKbt26kZmZaeN34wOWBScOw/td4asHwXXu9ZC83Q6yjvJS85dICE3wXY254OnFT/P+qveJ6dSJYs89B3aNPnk8bO5+CwenTKFig6JcO7g6IeH+tz70jOYtKaW57YXXqNikxTkf3/aewYSER1z0KKBIQafflHzE6TCpkxrH3U1L212K5FPfrtpDixd/4Ps1/rf5rohdkpK8U+R37959xu27d+8+dd/ZlClThjlz5nDs2DG2bt3K4sWLyc7OpnTpsz9H79u3j//7v/9jzJgxLFq0iLS0NMqVK0eLFi3Izs5m7dq1ufdN+aN138CrtbwfL8R1Asc7HYkJjuLF5i/iMPLXdL3nf36e91ZNJLpjB4q98Lx9gQ/Y9ehj7BoxgqKlYrhxWB1iioTZVsv5nGrectRDh4H3n7V5S9VW7ShTq66mb4pcAoW9fMYwDB5oW4HKydF2lyL51P7jWdwx4Rce/HQZWS63RvmkwCtVqhRJSUl8++23p247cuQIixYtokGDBhf8+oiICIoWLcrBgweZNWsW11133VkfN2TIEIYMGULx4sVxu91kZ2efui9nDWDAsSxwZ8OMIfDBDXB838V/7e7lOP83jBpFajCo5qC8qzGPjPxlJO+smEB0u3Ykv/gi2Nh19dCkSWy5vTeRUQ5uGlaH4uXjbKvlfNIPZ/H+IwtZ9t1W0uo14raRr5Fc/ioA4oom06L33XrNErlE6saZD7ncHrYfyqDdy3PJyA7AiwPxmZSEcF7rXpNKydHq1ikB7dixY6xbtw6AGjVqMGrUKFq0aEF8fDwlS5bkueee49lnn+Xdd9+lVKlSPPLIIyxbtoyVK1eeasLSsmVLOnfuzMCBAwGYNWsWlmVRvnx51q1bxwMPPEBoaChz584lKCjojPPPnj2b4cOHs2DBAkzTZNu2bZQrV46pU6eydetWHn74YbZu3UpYmH+OulwWy4I9q+Dj22DfFYxadpsEFToy+PvBfLvl2ws/3s8MqjGIPpXv5Og337D9vvvBxn0VnYmJpE6bhiM2ljmT1rBy3g7barmQpDIxXDOgCsGhThZOnUzpWnUpXDIF0+F/U1FF/JnCXj7l9lhM+WUrw6b+YXcpks85TIN7mpdhSKs0TAOFPglIP/zwAy1a/HMd0G233caECROwLIvHHnuMN954g0OHDtG4cWPGjh1LWlraqcempqbSu3dvHn/8cQCmTJnCsGHD2LZtG/Hx8XTt2pWnnnqKmJiYM86RkZFB9erVmTx5MtWrVz91+1tvvcXw4cMJCQlh7NixZ2zhkK9ZFmDB/NHw/QjvyN4V8gxZQWZ4AjfNuIlNRzZd8fF8bUD1AfStcjfHvv+ebYOHQPaV/5tctuBgUqdMIaxCeZZ8u4WfPlln1y4RF2Q6TToNrEpy+TiwLK3TE7kMCnv5XN+JvzBrxe4LP1DkAqokx/BajxoUjwvHVOATkcthWbB/HUy7G7b/lnvHjUzE9a+lbE3fxc0zbibDlZF7x/aRflX70b9qP47Nncv2ewdh2Rn4gORRo4hq344tK/Yz660VZJ/wz5lCyWmxXDekht6IFLlMCnv5mMdjcSzTRatRc9hzNMC7uIlPhAaZDG6Zxl1NS2uUT0QunmWBxwM/PgvzXsqV0bx/SGuHu9skZm2axYNzH8z94/tAnyp9GFR9IMfnzmfbvfdiZWXZWk9C37spNOhfHNqTwYxXl3J0/3k6pNogNCKI7o/VIzQyCFOtyEUui8JePudye1i88QA9xi/y22kYkv9ULBrFyBuqUSk55sIPFhHZsQSm9YW9q/P2PO2eg/r9eHrR03y4+sO8PVceuaPSHQyuMYjjCxawrf8A2wNfRPPmJI8eTXaWxYzXlrF74xFb6zldh/5VSKmUgOnQ9E2Ry6WwFyCenLGS8fM22l2GBBDTgB71UhjesSLBTlOjfCJyppxOm7OHw+I3wfLRJtd95+JKvIre/+vN0r1LfXPOXHbbVbdxf80hpC9ezNZ+92DZvMdicKlSpEyeghEezrfvruLPn+1fHlK5WTLNupe3uwyRfE9hL0Bkuz1c9+p8Vu70n3fkJDAkRocw4rrKtK6UhGVZCn0iBZ1lgWHAhjnwxUA4tMW35w8Kx33/ag5a2XT9oisHThzw7flzSY+KPXiw1gOk//ILW/v2wzph7xRKMzyc1GnTCEkpyc8zN7J4xkaw6QoxvlgENz1cB9Nh6DVH5Aop7AUIl9vDlgPpdBg9lxPZPnp3VQqUVhWL8FzXqsRHBOvFV6SgsizIPAJfPQhLbZxGWbQarj7f8vueJdw1+y7cln82F7mQ7hW6M6z2g6T/9htb7+6LlWF/45kSb4wjsmlT1v26h28nrMTl42uKoBAHN/2nDtEJoZq+KZIL9FsUIJwOk5SECJ7pXMXuUiRAfbNqD02f/56352/E47G0sa1IQWJZ4HHDwtfg5Sr2Bj2AnUtxzn6U2km1GVhjoL21XIEPV3/Ik4ufIrxmTUq+9SZGeLjdJbH17r7se+NNStcoTOcHahIeHezT87foWYHoQmEKeiK5RCN7AejRz5fz3oLNdpchAaxKcgxPd65MleKxmtopEshypmxumgcz74O9a+yu6Ey3fAxpbRj03SC+3/q93dVcthvK3cCj9f5DxrI/2NqnD57j6XaXRPQ1HUl65jlOpLuYMWYp+7Ydy/Nzap2eSO5T2AtAbo/FTeMW8Ovmg3aXIgGuU9WiPNqpEoUiNbVTJOBYFhzbDV8OhVXT7a7mnDz3reJEWCw3TL+BrUe32l3OZbu+7PU8Ue8xTqxYyZY77sBz/LjdJRFSsSIlP/gAnMF8PX4FG5fuy7NzFUmNousDtTBMrdMTyU0KewHI7bE4lJ5Fu1fmslf770keC3Ga3NGoFINblVPXTpFAYFngccGPL8BPoyHb/nVk5xWdjGvQb2w6uo3uM7tzwu1fe8Vdik6lOzGiwRNkrlrFltvvwHMs70fTLsSMjaXUZ58RlFiEBdPW8/vXud+QJyTCSbdH6hEeFaTpmyK5TGEvQLncHpZtO8zNbywg263/xZL3CkUGc3+bNG6uXRJDG7KL5D85UzZXzYBZw3zfZfNKVOiE+6Z3+XLjlzw872G7q7kiHUt15OmGI8hcs4YtvW/Hc/So3SWBaZIy8T3Ca9Vi1U87+OGDNXhy69rCgGsGVqNEhTgFPZE8oN+qAOV0mFQvEcvwjlfZXYoUEPuOZTFs6nLavzKXhRv2A6iJi0h+kPN7um0xvNUKJvfIX0EPYPV0HL++Q6cynbgx7Ua7q7kiMzfO5MH5wwgpX56U997FjImxuyTweNjcoycHPvqICvWLct2QGoRGBOXKoet0SKXkVfEKeiJ5RCN7BcCQyUuY9vt2u8uQAqZ5WmEev/YqUhIiAI30ifidnJG8Pavh64dh3bd2V3TFrH4/4SqSRq+verF833K7y7kirVNaM7Lxc2St38CW23rjPnTI7pIAiL35ZhIfeYRjh7OYPnoph3ZffjOZlMoJXDOwWi5WJyJ/p7AX4CzLIttt0XnsfFbs0Ibr4lsO0+Dm2iW4r3U5EiJDAIU+EdvlvOwf2QGzH4EVU/+6Lb8LjsR9/yr2u0/Q9YuuHMo8ZHdFV6RliZaMavoCWRs2saVXL78JfGG1alFi/HjcOPjq9T/YtvrSG8JFFwrj5uF1CAp2YJh6XRDJKwp7BYDL42HPkUzavzKXwxnZdpcjBVCww+Sm2sUZ0jpNm7KL2MmyIOMQfPcE/PaetxFLoClWC9eds/hl96/0/aYvHsu3m4LntuYlmvNK01Fkb9rM5l634T5wwO6SAHAmJpI6dSqOuDh+/GgtK368+BlEzmCTG4fVIbaI9tMTyWsKewWEy+1hwYb93Pb2Yjz6Py42yQl997VJIy7cu1Gvgp9IHst5mc9Ohx9HwqLXvX8PZA0HYbV+gjeWvcGrS161u5or1iS5CWOavYJr2zY297wV9/79dpfkFRxMqckfEVqxIsu+28q8T9ZhXcRFRus7rqJs7URMjeiJ5DmFvQLEsizGfLeOUbPX2l2KFHDBDpOb6pTgvtZpxIV7F/kr9InkspyX9xOHYd4o+OVtyPSDzo6+0nMalL2a/t/0Z+72uXZXc8UaFWvEa81H49q+wxv49uXdnneXqtjIkUR37MDWVQeY9cZysk64z/nY6q1K0OiGcj6sTqRgU9grgO567xdmr9xtdxkip0LfkFbliI/QSJ9IrshpvHJ0N/z4PPz+Prjy795zl80w8dy3ivSQSG6cfiPbjm2zu6IrVj+pPq9f/RruHTvZfOutuPbstbukUxL69KHQkCEc3pvB9DFLObr/nz9zKZUT6Digqp7nRXxIYa+A8VgWJ7LdXDN6Hhv2Hbe7HBHAuzH7TbVLMKhlWQpHhWJZli4GRC5VTsg7uBl+eAb++Dgw1+RdipgSuO79lQ1HN3PLzFvIdGfaXdEVq5NUhzevfh33rt1s7tET1549dpd0SkSTJiS/9hrZ2TBz7DJ2rT986r74ohHc8FBtnEGmGrKI+JDCXgHkcnvYciCd61+bz5ETBfxCQPyK0zRoVzmJfs3KUDk5RqFP5GKc2kJhJfzwLKyaDvm8KUmuqtQZd9fxfLH+Cx796VG7q8kVtRNr82bLcXj27PUGvl277C7plKCSJUn95BOMiAi+e3cVaxfvJjQiiJv+U4eImGA1ZBHxMYW9Asrl8fDLpoPcOn4R2W79CIj/qVkylrublqbNVUkYhqZ3ivxDTsjbMAd+Gg3rvrG7Iv/V6RWo1ZvHfnqMqX9OtbuaXFGjcA3ebv0m1t79bOp5K64dO+wu6S9hYZSeOpWQUqn8+tUmipaLJalUtIKeiA0U9gowj8fi8yXbGTJlqd2liJxTcmwYtzVM5db6KYQGeS8UFPykwMp5yXZnwq/vwuI3YP86e2vKJ6z+i3AllKHnlz1ZeWCl3eXkiiqFqvBe63ew9h9gc8+eZG/3o8AHFH/9dSKbNQULTd0UsYnCnvDKN2t56Zs/7S5D5Lwigh3cWLsEfZuWpmhsmKZ4SsFyaj3eFlgwBpZ+WLA6a+aGkGhc961iv+s4Xb7owpGsI3ZXlCsqJ1TmvTbvwMHDbO7Rk+xt/tOIJv7OO0l8YKjdZYgUaAp7AsADHy/l41/95wVC5FxMA1pVTOT2Rqk0KFNIoU8CW87auz+/hUX/hQ3f/TW6J5eueF1cd3zFop2LuOebe7AIjH/Lq+Kv4v2278Lho97At2WL3SUR1bo1xceMtrsMkQJPYU+wLAuPBb3eXsT8dX6yUavIRSgeF8aNtYrTo34KhSJDFPwkMOSM4p04DL+9590f78AGu6sKHI3vx2r5CGOXjuX1pa/bXU2uSYtL46N2H2AcOcbmnreStWmTbbWEVq5MygfvYwQFYZhapydiJ4U9AcDt8W7J0GXsT6zZralBkr+YBjQqW4judUvSplIijpOBT8FP8o2cl2LLA2tnwe/vwZ+ztXVCXrntCzypTej/TX/m75hvdzW5plxsOT5q/wHm0XRv4Nu40ec1OIsWpdTUT3FER2M4HD4/v4icSWFPTnG5PRw4nkWnV+ex+0j+34tICqbY8CCuq57MrfVTKFskUqN94t8sDxgm7FkDv77t3RsvXTMs8pxh4r5/DenBYdzwxQ3sOO5fjU2uROmY0nzc4SPM4yfY3KMnWRt8NypsRkSQOvkjglNTMZxOn51XRM5NYU/O4HJ7WL/3OF3Gzud4ltvuckSuSKVi0dxcpwRdaxYnIsSp4Cf+4dQ0zSOw5H1YMgl2/WF3VQVPXCquAYtZd3gjPb7sQZYny+6Kck1qdCqfdJyC4/gJNt/ai6x1ed+x1QgKosRbbxFeu5ZG9ET8iMKe/IPbYzF/3T7umPAzLo9+PCT/C3IYNCpTiPZVinJN1aIKfuJ7OQHPdQJWTocVU2HdbHBn211ZwVblRjxdxvHp2qk8sfAJu6vJVSlRKXzScQpBJ7LZfOutZK7Nw67bhkHyS6OIatNGa/RE/IzCnpyVx7KY/PNWhk3Vu80SWJymQYMyCXSsUpRO1Yop+EneORXwMmHVTFjxqXfjc9cJuyuT0103Fmr0YPi84Xy+/nO7q8lVyZHJfHbNpwRlutl8ay8y16zJk/MkPjyMuFtv1fOoiB9S2JPzev5/qxn7w3q7yxDJE07ToF7peDpU9ga/6LAgPJaFqQsWuVw5a/CyjsPKL2DVF7D+OwU8P2cN/JnsuFRumXkLaw7mTSCyS3JEMtM6fUpwloctt/XmxMrc3VBee+mJ+DeFPbmgQR/+zhdLA2fxusjZOEyDuqnxtK+SRNtKiSRGezduB3X1lPPIeQk1DDi6C1bPhFXTYdNcddLMT0Jjcd23gr1ZR7lh+g0Bs+F6jqLhRfms01RCXLDltts4sSJ3Al/MdddR7Llnc+VYIpI3FPbkvDyWhdtj0evtxSxYrw5xUnCUKhRB07TCXF2+MA3KJBDsdGi6p3jlTM90Z8OmebD2f97pmfvzvgmG5KGSDXH1ns5PO35i4LcDA2bD9RyJ4Yl80WkaIW6TLb1v58QfV7ZMI7JFc4q/+iqYpp4XRfyYwp5ckNtjke320P2Nhfy+9ZDd5Yj4XLDDpHZqHE3TCtPmqkRKF44EUPgrKE4fvTu4GVbPgHXfwub5mp4ZaJr9G1r8h9G/jebNP960u5pcVzisMNOv/Zwwj4PNt9/BiaVLL+s4YbVrU/Lt8RhOpxqyiPg5hT25KG6PRUaWmxvH/cSqndp0XQq2wlEhNC1XiGZphWlWvjAxYcGAwl/AyBm5Azi2Fzb+AJvme0fvDm+1tTTxgdu/xFOyAf1m92PBzgV2V5PrCoUWYvp1nxPucbLlzjvJ+H3JJX19SIUKpE76ACMkRFssiOQDCnty0VxuD0dPuOj635/YsO+43eWI+I0S8WHUKhlPrZQ4GpdLICU+AtM0tOYvPzh91M7ywIFNsOE72LIAtiyEw9tsLU9sYDpx37+G485guk7vyq7ju+yuKNfFh8Yz49oviCCYLXf2IeO33y7q64JSUkj96CMcUZHaNF0kn1DYk0vicns4cDyLzmN/YvuhDLvLEfFLEcEOqpWIpVZKHPVKeUNgWLBT4c9ulgVY3m6ZAK4s2LUMNv7oDXdbF8OJQ3ZWKP4ioQyuexaw5tA6bv3qVrI9gbcfYlxIHDOu+4JII5Qtfe4i45dfzvt4Z5HCpE6ejLNwYQU9kXxEYU8umcvtYdeRE3Qe+xN7j2baXY6I3zMMKFs4klopcVROjqFScjQVE6MIDfZeMCkE5oHTR+wAMo/DziWw4zdvwNu5DPb/CR63bSWKn6vWHc/1Y/l4zceMWDTC7mryRExwDDOvn06UGc7Wu+8mfdHisz7OkZBA6gcfEFQ8WUFPJJ9R2JPL4nJ72Lw/na6v/8Sh9MB7x1PEF4rFhFI+KZoKSVGUT4qieokYSsSF43B4R548loWBQuB5/T3UARzbA9t/g52/e0Pdrj+01k4uT5c3oepNDJs7jBkbZthdTZ6IDo5m5nXTiXZEsLVvP9IXLjzjfkdsLCkfvE9wSoqCnkg+pLAnl83l9vDnnmPc/MYCjmRoPymR3OA0DUoVijgZAKNJSQindKFwSsRHEB0WdOpxpz91B3QYPP0l6vTvM+MQ7PsT9q72bnlwYP3JjxvVIVNylXXvb2TFFueWmbew9uBau8vJE5HOSGZ2nkGsM4pt99zD8fk/AWBGR5MycSIhZUor6InkUwp7ckVcbg+rdx2l+xsLOZqpwCeSl0KDTJJjwykRF0bxuDCKx4VTPC6MMoUjSY4LOyMM5vj7U7xfBMMzarIA48wg53Z5184d3gaHNsPRnd4Nyw9tgf3rvcEuU12BxUfC4nENWc7uzEPcOP1GjmYH5s9euDOcL6+fSXxwDFvv6U/GkiWUfHcCoeXLK+iJ5GMKe3LFXB4PK7Yf4ZY3F3I8S+tfROwSFuQgITKYuPBg4sKDiIv4598LRQZTOCqUuPAgokKdBDvP0jr99K0HrpTbBVnHIesYZB6B9P3ePyeOQOZhOHHYG+SO7oQjO70fMw78LRCK2Cy1Ce5en/Hj9rkM+m6Q3dXkmXBnODOvn0FCcCxZmzYRXLq0tlcQyecU9iRXuD0WS7Yeoudbi8jIVuATyS9MA0KcDkKDTEKDHIQGOfhiQAOidvwEPzwNZhA4gsB0gunwhjfPhf64vQEv8wi41MRJAsTVw6HpA7z060u8vfxtu6vJM/Gh8XzTdTYOy8AM+udsARHJXzQuL7nCYRpULxHLO7fXofc7izmR7bG7JBG5CB4LMrLdJ9+k8TZbMiwLju3ybkUgIl7fjYDUpvyr5r9Yvm85i3cF3u9HmDOMl1u8jGk6ME2N6IkEAtPuAiRwOEyDOqnxjL+tDiFO/WiJ5FcOE+8edCJypgkdsDIOMqr5KIqEF7G7mlwV5gxjXOtxVC1UFYeCnkjA0BW55CqHaVC/dALv3lGXiGC9WIjkR6YBuBX2RP7B48Ixvi0RzjBebv4yTjMwJkgp6IkELoU9yXU5I3wf3V2fmLN0BxQR/+YNe1prJ3JW+//EOeM+KhWqxNDaQ+2u5opFBkXyZus3FfREApTCnuQJh2lQsWg0n/RrQOHIELvLEZFLYBqGpnGKnM/vEzFXfEaPij1oX6q93dVcttiQWCa0m0DlQpUV9EQClMKe5Bmnw6RUoQim9m9IcmyY3eWIyEUyDEMjeyIX8snteA5u4smGT1Imtozd1VyywmGFmdh+ImViyyjoiQQwhT3JU06HSdGYUKb2b0jpQhF2lyMiF8Eb9rLtLkPE75lvXo3DcjPm6jFEBOWf17hiEcV4v8P7FI8qHjDrDkXk7BT2JM85HSYJEcFM7d+QikWj7C5HRC7I0P54IhcjfT/Oj26hWERRRjQaYXc1FyU1OpUPOn5AkfAiCnoiBYDCnviE02ESGeLk474NqVky1u5yROR8DFPTOEUu1vrvccwfQ6uUVvS6qpfd1ZxXWlwaEztMJDYkVkFPpIBQ2BOfcTpMwoIdTLqrPg3LJNhdjoicg2GaatAicim+fRy2/cJ9te+jVmItu6s5q6qFqvJuu3eJDIpU0BMpQBT2xKccpkGQw+TdO+rSqmJgbUgrEgjCg0++LGifPZFL8057OHGEl5q/RKGwQnZXc4Y6SXUY33Y8Yc4wBT2RAkZhT3zOYRo4TINxt9bm2mrF7C5HRE4TEXzyQlBhT+TSuLNwvN2OqKAIXmr+Ek7DP0LV1SWvZlyrcQSZQeq6KVIAKeyJLUzDwDTg5W7VuaVuSbvLEZGTIkNOXqCqQYvIpdu7GueX/6Zq4aoMqTXE7mq4qfxNvNT8JRymQ0FPpIBS2BPbGIaBaRg83aUK9zTLf3sUiQSi8JCTF4Rq0CJyeX59B3PVDHpV6kWblDa2lTGw+kAeqf8IpmFiGrrcEymo9NsvfuHB9hV4qnNlnKZhdykiBVp4zjRONWgRuXxTbsVzaAsjGo+gVEwpn57aYTh4ouET9K3W16fnFRH/pLAnfqN73ZJMuL0OUSH+sc5BpCCKCNGaPZHcYL55NUEWjG4xmjBnmE/OGeYM47WWr3Fd2et8cj4R8X8Ke+I3TMOgfpkEPhvQiOJxvnlhFJEzhQdrGqdIrji+F+fknpSIKsGTDZ/M89MlhCYwsf1E6hetr2mbInKKng3ErzhNk5SEcKYPbEz1ErF2lyNS4IQFnQx7msYpcuXWfYNj4VjalmpLj4o98uw0pWJKMfmayZSJLaNGLCJyBoU98TtOh0l0mJMpfRvQvnKS3eWIFCinwp5G9kRyx9fDYcfvPFD7AaoXrp7rh6+dWJtJHSaREJagPfRE5B8U9sQvOUwTp8Pgvz1rqVOniA+FnZrGmW1vISKB5O12kHmUl1u8TEJoQq4dtnPZzrzZ5k1tli4i56SwJ37LNLydOR9sX4Hnu1YlyKFOnSJ57VTY0z57IrnHdQLHhI7EBEfxYvMXcRhXNtXSNEweqP0ATzR6AoehPfRE5NwU9iRfuKFWcSbeWY/oML1zKZKXQjWNUyRv7F6O838PU6NIDQbVHHTZh4kMimRsy7H0vKon4N2zVkTkXBT2JF8wTYPaqXF8MbAxJePD7S5HJGCFOtWgRSTP/Pwm5pr/cUflO2hZsuUlf3nxqOJ82PFDddwUkYumZwrJN5ymSfHYMKYPbEytlDi7yxEJSKFBJ18WtM+eSN74qDuew9t4pvEzpESnXPSX1Umqw5RrplA8qrimbYrIRVPYk3zF6TCJDHXy0d31ubZaMbvLEQk4ITkjewp7InnGfLMlQVz8hus3pt3Im63fJNwZrkYsInJJFPYk33GYBg7TYHT3GgzvWBGnqfUKIrklJMjEUidOkbx1bBfOT24nJTqFxxs8fs6HOQwHD9V9iEcbPIppmBrRE5FLprAn+VJOp847GpdiSt8GFIkKsbkikcAQ7DC17YKIL6z5CsfiN+lQugPdynf7x91xIXG80foNulfoDqgRi4hcHoU9yddMw6Bq8RhmDW5K/dLxdpcjku+FBJngUdgT8Yn/PQg7l/Fg3QepWqjqqZsrJVTik2s/oWZiTTViEZEromcQyfecDpPoMCeT+tSnb9PSdpcjkq+FOB1aryfiS2+3xcg6zitXv0J8aDxdynVhYoeJJIQmaH2eiFwxw7Isy+4iRHLT7JW7uG/yUo5muuwuRSTf+fSeBtSMzcB46Sq7SxEpOIpWw9XnWw5mHqJweGEsy9K0TRHJFRrZk4DTokIRvvxXEyokRdldiki+E+QwtaG6iK+dOIzz6E5iQ2IBrc8TkdyjsCcBx2maFI0J5fOBjehSM9nuckTyFTVoEfGx8h2g33yIKkqQI8juakQkwCjsSUByOkyCHSajbqrOU50rE+LUj7rIxXA6THCdsLsMkcBnOqH1E9D9QwgKBwU9EckDugKWgJUzDaZbnZJ8ek9DisddeONakYIuyGGAS9M4RfJUVBLc/iU0vNf7uanLMRHJG3p2kYDnMA0qJEXx5aAmNE8rbHc5In7NaWpkTyRPlW0J9yyAYrVA2yqISB7Ts4wUCE6HSUSIk7dvr8O/25b3jl6IyD84HQZkK+yJ5DpnKLR7FnpOhdAYcGhbBRHJewp7UmA4TAPTMOjXvAxfDGxM2SKRdpck4necJhjqximSuxIrQd+5UPdu7+emw956RKTAUNiTAsc0DMoVieTLQU3o3TAVdbgW+YvTQJuqi+QWw4D6/eHuORBfWiFPRHxOYU8KJKfDJNhp8vi1lXj/znokRofYXZKIX3AYqEGLSG6ISoJbP4N2z3g7bWrapojYQGFPCrx6peL55r5mdKxS1O5SRGznMNHInsiVqtAR+i+ClMZ2VyIiBZzCnhR4TodJRLCT13rU5KWbqxMVondfpeAyNY1T5PIFhUOnV6DbJAiJ1mieiNhOYU8EME3vwr1rqxVl9n3NqFcq3uaKROxhGtpnT+SyFKsB9/wENW71fq6980TED+iZSOQ0DtOkcFQIH95dn2HtKxDs0K+IFCwa2RO5RIYJje+DPt9ATAk1YRERv6IrWZG/ydmi4a4mpZk5qDHlE6PsLknEZwyN7IlcvEJpcOfX0PJRMJ2atikifkdhT+QcTNOgVKEIZgxqTJ8mpbRFgxQIhmGAO9vuMkT8m+mEpkO90zaL1kAvECLirxT2RM7D6TAJcpgM73gVn/ZrqI3YJfAZBmhTdZFzK1YD+s2DFv/Rlgoi4vcU9kQuUtXiMfzvX024r3UaIU796kigMjWNU+RsgsKg9ZNw13dQqJx3rZ6IiJ/TM5XIRXI6TJwOk4EtyjL7vmY0KJNgd0kiuc4wTTVoEfm71CYwYDE0GOANeaZG80Qkf1DYE7lEpmmQHBvGh3fVZ+SNVYkLD7K7JJFcERF88gJWYU/EKzQGOo2G3jMgOlmdNkUk31HYE7kMjpP78l1fI5kfHmhB15rJNlckcuUiQ09eyLoU9kSo0BHu/RVq9PB+rqAnIvmQwp7IFXCaJlGhTl68qTof3V2f1IRwu0sSuWzhQScvZtWgRQqyiMJw47vQbRKExWvKpojkawp7IlfIPNlyu3ZKHLOHNGPg1WUJcqgNt+Q/ESEnL2rVoEUKIsOEWrd7R/MqXuO9TaN5IpLPKeyJ5BKnwyTIaXJf6zRmDW5K7ZQ4u0sSuSThITkje9pnTwqYkvW92yl0ehlCojWaJyIBQ2FPJJeZhkHJhHA+uachT3euTHSYLhokf4gIPtlsSNM4paCIKgpd3oQ7ZkHh8t7btEG6iAQQhT2RPOA0vb9aN9cpwZyhLehWpwSmrh/Ez4UFn3xJUIMWCXSOYGg0GAb9DpW7eG/TaJ6IBCCFPZE85DBNYsKDeLZrVWYNbkrjsoXsLknknNSgRQqEcm1g4C/Q6jHvRukKeSISwBT2RPJYTgOX0oUjeb9PPSbcXocyhSNtrkrkn8KCc7ZeUNiTABRfGm75GHp8DDHFvQ1ZREQCnJ7pRHwkZ2++xmUL8fWQpvzftZWIjwi2uSqRv5wKe2rQIoEkOAJaPQ4DfoYyV3tvU5dNESkgFPZEfMzpMHGYBj3ql2Tuv1twV5PSBDv0qyj2C9U0Tgk0VW6AQUug4SBwOL1/REQKEF1hitjEaZpEhDgZ1qECPzzQnA5VkuwuSQq4sCBN45QAUbo59J0LXcdDRCGN5IlIgaW3uERsZhoGidGhjO1Ri9+3HOTxL1awdNthu8uSAuivkT1145R8qlhNaP1/UKopeFze27Q2T0QKMD0DiviBnPV8VZJj+HxgY17pVp1iMaE2VyUFTajz5EuCwp7kN4XKwU0T4e7voWRD723qsikiopE9EX/iPLl2r2OVorSvXJS35m5g3I8bOJyhhhmS90KC1KBF8pnoZGj+ENToCR639zatyxMROUXPiCJ+KCf09W1Wht6NUhk/byNvzd2o0Cd5KthpYrmzMOwuRORCwuKgyf1Qry9geqdqqtGViMg/KOyJ+DGHaRAe7KR/87Lc2biUQp/kqRCnqVE98W/BEVD/Hmh8HzhD1XhFROQCFPZE8oG/h7635m5k/DyFPsldIU6Hwp74J0cQ1OoNzR+G0BiFPBGRi6SwJ5KP5IS+AS3K0qeJQp/krmCnqeYs4l+CwqDGrdDkPohMBAwwNNFYRORiKeyJ5EMKfZIXgh0muDPsLkMEQqKhTh9oNMg7kgfaQkFE5DIo7InkYwp9kpuCHIY2VBd7hSd41+TVuweCwsFUwBMRuRIKeyIB4PTQd2fjUrw1byNvK/TJJQrSNE6xS3QxaHgv1L7Tuz+e1uSJiOQKhT2RAOIwDSJCnAxsUZa7mpRi0qItTPhpE9sOamqeXFiQaWpkT3wrvjQ0GgzVe4CBNkIXEcllelYVCUA5I329G6ZyR6NSfL1yF2/O3civmw/aXZr4MafDgBMKe+IDiZWg8f1QuTN4PNoIXUQkj+jZVSSA5WzO3qpiIu0qF2X59sO88eMGvvxjJy6PZXN14m+CHCa4TthdhgSyUk2hwUBIa+vd5kOboYuI5CnDsixd8YkUEG6PB4dpsufICd6ev5FJi7dwJMNld1niJxY/3JLCu37EmHSj3aVIIAkKh2rdvI1XCqWB26WRPBERH9GzrUgB4jjZ2a5wVAj/bluBwa3SmPLLVt6Zv4mN+47bXJ3YzWmC4dY0TsklcaWgbh+o2RuCI4CT7y0r6ImI+IyecUUKIMMwMAwINR3cUrckPeun8P3qPbw1dyMLNuy3uzyxicNADVrkyhgGlG4B9fpBudZgeU5ruqLN0EVEfE1hT6SAy1nX1yytMC0rJrJm11He+HE905fuJMvtsbk68SWnCWhkTy5HcCRU7w71+3s7bLpd3vV42ghdRMRWCnsiAvwV+soWieTFm6rzaKdKfPzLVqb8spW1u4/ZXJ34gndkT/vsySVIKAN174YavSAo9NRMTU3VFBHxD3o2FpEzOEzvVKuYsCB6N0ylT5PS/LHtMB8u3sL0pTs4mqmGLoHKNNCm6nJhjmAo3wFq3QZlrj6z4YpmaoqI+BWFPRE5p5zRvquKRfNU58o8du1VzFy2k8k/b2XRxgM2Vye5zVDYk/MpVgOq3wJVu0FoNHhOvvGjUTwREb+lZ2gRuaCc0b4Qp4NO1YrRpWZxth1MZ9KiLXz62zZ2H9E6r0BgGoYatMiZIotAlZugZi8oXN67N54jyHufqUsIERF/p2dqEbkkQSdH+5Jjw7i/TXmGtinPj3/u5cPFW/lu9W6y3dq6M78yDEMNWsQb5tLaQY1boWwr722G8dd9IiKSbyjsichlMQzD29ADaFy2EM3LF+FQehYf/7qNKT9v5c89auqS33jDXrbdZYhdkqpCjR7eaZphsd61eKbD7qpEROQKKOyJyBXLWdsXGx7M7Q1TuatJaVbtPMIXS3bw5fKdbN6fbnOFclE0jbPgiUqCSl280zSLVDxzmqbW4omI5Ht6JheRXJUT/MonRjG0bXkebF+B1bu8we+r5bvYuO+4zRXKORmmGrQUBJGJcNW1UPkGKFHXu11CThdNTdMUEQkoCnsikidM868e7GmJUdzfpjz/bleBtbuPMn3pDr78Yyfr9yr4+QvTBMMwNbIXqCKLQMWTAa9kPe9tlnVy43N7SxMRkbyjsCciec40jFMXlOWKRDK4VRr3tynP+j3H+GLpDmb+sZN1WuNnq4jgky8HGtkLHNHJUPEauKrzPwMeKOSJiBQACnsi4lOnN3YpXTiCQS3LMaR1Ghv3HeeLJduZ+cdO1u5W8PO18OCTjTgU9vK3hDLeEbyrOkOxauDxAAp4IiIFlcKeiNjm9OCXmhDOgKvL8q9WaWzef5wZy3YyZ+1eftt8EJdH2znktYiQky8HmsaZv5hO77q7sq28Ia9QOfC4/9oqwTTtrU9ERGylsCcifsEwDJwnL1BLxofTt2lpBrQoS3qWi/nr9jFnzV7m/LmXrQcybK40MP01jVNhz+/FlICyLaFsayhzNQSHe7to5mxyru0SRETkJIU9EfE7hmHgPDnkFx7s5OoKibSsmIhpGGw7mM63q/bw4597WbB+P+lZbpurDQwRITnTOLXPnt9xhkJKI2/AS2vnnappebx/cgKeumiKiMhZKOyJiN9znNbZs3hcOLfUK8ltDVNxuT38tuUg36/ey49/7mXlziNYmvF5WcKDNI3TrySU9U7NLNcGUhuDM+TMPfAM8691eCIiIuegsCci+U7Qyb38nA6T2inx1EqJ58H2FTiYnsUPa/YwZ80+5v65l/3H1WzkYqlBi82ik6FkfW+wK9cWYpL/aq6SMy1To3ciInKJFPZEJF87fT+/uPBgrqlajM41igOwaf9xFm04wG9bDvLr5oOs33tMI3/nEJYzjVMje3nPMKBwBSjZ4GTAawLRxbz3nT56p+YqIiJyhRT2RCSg5Iz6AaQmRJAcG8ZNtYtjGAbHTrj4dfMBftnsDX9Ltx7iuNb8ARrZy1OOYChWA1IaQsmGkNIAQqK8a+48HnCc9lKs0TsREclFCnsiEtBOD3+RoU6alCtMo7KFcDpMPB6LP/ccY9HG/fy2+RC/bjlQYLt9hgYp7OWasDgoXsc7cpfaGIpV9wY+jwsw/pqWaZjg0OidiIjkHYU9ESlQTNPAPLmztGkalE+KonThCHo1SAXgwPEsft50gF82HWTJ1oOs3nmUo5kuGyv2jVCnpnFeluhkSKoCRatCUjVIrnnmlEzTedqed3rJFRER39Irj4gUeKeP/sVHBNOqYhFaVUw81QV0z5ETrNx5hFU7j7B611HW7DrK+r3HyHYHzgLAMI3snZ9hejtk5oS6otW8I3ahMd773S5vqDt9jztNyRQREZsp7ImI/I3jb40xikSHUigyhEZlC50Khi6Ph63701m+IycAej9uP5SRL5vAhAZrZO+U8HiILwOJlSCpqne9XWIl7/YH4A3EZtBfI3Zw5ro7ERERP6FXJxGRi3D69E8Ap2lSqnAkJRLCaV85CefJEJiR5WbtnqOs3HGENbuO8ueeo2w9kMHOwxl+PRIY6jwZcAvKyF5IlDfQJZz8E1/G2yEzvjSERnsfY3m86+wcwWd+7d8/FxER8VMKeyIiV8D5t1HAsGAH1YrHclXRaByGcWprCI/HYt+xTLYeSGfTgXS2Hcxg28G/Pu48dAKXx74wGOw0sSwPhieA1icGR0BsyslAV/ZkoCvv/Tw84a/HubMB45+jc4apYCciIvmawp6ISB4I+luXRdM0KBIdSpHoUKqViMUCnKaBYVwgDB7IYMfhDA4ez+LIibwLYqFBjpOhJx9wBENkIkQXhaiiEJV08mNRb8OUmGSITILg8L++xuMCyzr7OjqtrRMRkQClsCci4mPOs7Tbv1AYBHB7LI6eyOZQejYHjmex91gmB9OzOHg8i4Pp2Wf8/VB6FgeOZ3E4I5uLGTAMdpr2hL3gCAiJ9jY6CY0+7e8xJ/8eDRGFvUEupoQ32IXFnnkMjxssN97RuXMEN3XCFBGRAsiwrPzYSkBERDyWhdtjYVlgGmcPkR7L4nimi8MZ2WRkuTmR7SYj2016lvfjiWwPJ7LdNCtXmKJRJsaCV72hz5Pt7TDpyT75ucs7rdF0eIOT6fQ2KTn9c0fQue8PjvDuPxcWdzLERUFQxJndK09nWd4Qhwfv3nTOMxuiiIiIyAUp7ImIFFAeyxsUPZYFFjhxeVvQ/PWfkwHL+CtoWRZg8VfLUeuMD6f8/RiGQ2FNRETExxT2REREREREAtA/5/yIiIiIiIhIvqewJyIiIiIiEoAU9kRERERERAKQwp6IiIiIiEgAUtgTEREREREJQAp7IiIiIiIiAUhhT0REREREJAAp7ImIiIiIiAQghT0REREREZEApLAnIiIiIiISgBT2REREREREApDCnoiIiIiISABS2BMREREREQlACnsiIiIiIiIBSGFPREREREQkACnsiYiIiIiIBCCFPRERERERkQCksCciIiIiIhKAFPZEREREREQCkMKeiIiIiIhIAFLYExERERERCUAKeyIiIiIiIgFIYU9ERERERCQAKeyJiIiIiIgEIIU9ERERERGRAKSwJyIiIiIiEoAU9kRERERERAKQwp6IiIiIiEgAUtgTEREREREJQAp7IiIiIiIiAUhhT0REREREJAAp7ImIiIiIiAQghT0REREREZEApLAnIiIiIiISgBT2REREREREApDCnoiIiIiISABS2BMREREREQlACnsiIiIiIiIBSGFPREREREQkACnsiYiIiIiIBCCFPRERERERkQD0/9wb32jyMNhVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_data = df[\n",
    "    (df['src_ip'] == '10.0.0.18') & \n",
    "    (df['dest_ip'].str.contains('162.125')) & \n",
    "    (df['@sourcetype'] == 'stream:tcp')\n",
    "    ].groupby('bytes').size()\n",
    "\n",
    "grouped_data = grouped_data.sort_values(ascending=False)\n",
    "top_5 = grouped_data[:5]\n",
    "others = pd.Series(grouped_data[5:].sum(), index=['Other'])\n",
    "plot_data = pd.concat([top_5, others])\n",
    "\n",
    "plt.figure(figsize=[10, 10])\n",
    "wedges, texts, autotexts = plt.pie(plot_data, \\\n",
    "    autopct='%1.1f%%', startangle=90, \\\n",
    "        wedgeprops=dict(width=0.3, edgecolor='w'))\n",
    "plt.setp(autotexts, color='white')\n",
    "plt.setp(autotexts[:5], color='black')\n",
    "plt.axis('equal')\n",
    "\n",
    "center_circle = plt.Circle((0, 0), 0.70, fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(center_circle)\n",
    "\n",
    "plt.legend(wedges, plot_data.index, title='Categories', \\\n",
    "    loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correspond to  Listing 7.32 Humio search code – List the values of fields bytes and ssl_client_cipher_list for connections between 10.0.0.4 and IP addresses that contain 162.125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320044\n"
     ]
    }
   ],
   "source": [
    "df_original = pd.read_json(\"ch7_stream_events.json\")\n",
    "print(len(df_original))\n",
    "df = df_original\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bytes_in', '#type', 'dest_mac', '#repo', 'src_ip', '@sourcetype',\n",
       "       'endtime', '@timezone', '@rawstring', '@id', '@timestamp',\n",
       "       '@ingesttimestamp', 'timestamp', '@error', 'src_port', 'time_taken',\n",
       "       'cribl_pipe', '@error_msg', 'dest_ip', 'transport', 'bytes',\n",
       "       'transaction_id', '#error', 'dest_port', 'src_mac', 'protocol_stack',\n",
       "       'bytes_out', 'flow_id', '@timestamp.nanos', 'packets_in', 'app',\n",
       "       'packets_out', 'protocol', 'tos', 'fragment_count', 'version',\n",
       "       'protoid', 'connection', 'refused', 'tcp_status', 'client_rtt_sum',\n",
       "       'data_packets_in', 'server_rtt', 'server_rtt_packets',\n",
       "       'duplicate_packets_in', 'data_packets_out', 'duplicate_packets_out',\n",
       "       'ack_packets_out', 'ack_packets_in', 'missing_packets_in',\n",
       "       'client_rtt_packets', 'missing_packets_out', 'client_rtt',\n",
       "       'server_rtt_sum', 'initial_rtt', 'ssl_cert_self_signed',\n",
       "       'ssl_cert_sha1', 'ssl_cert_md5', 'ssl_validity_start', 'ssl_issuer',\n",
       "       'ssl_cert_sha256', 'ssl_publickey_algorithm', 'ssl_validity_end',\n",
       "       'ssl_client_hello_version', 'ssl_signature_algorithm',\n",
       "       'ssl_publickey_bit_len', 'ssl_version', 'ssl_compression_method',\n",
       "       'ssl_cipher_id', 'ssl_cipher_name', 'ssl_serialnumber',\n",
       "       'ssl_session_id', 'ssl_subject', 'canceled', 'site',\n",
       "       'http_content_length', 'server', 'uri_path', 'http_method', 'status',\n",
       "       'form_data', 'http_comment', 'http_content_type', 'uri_query',\n",
       "       'http_user_agent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53132     49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "53157     49196,49195,49200,49199,49188,49187,49192,4919...\n",
      "53170     49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "53175     4866,4867,4865,49196,49200,159,52393,52392,523...\n",
      "53218     49196,49195,49200,49199,49188,49187,49192,4919...\n",
      "                                ...                        \n",
      "319760    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "319807    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "319814    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "319870    64250,4865,4866,4866,4867,49195,49199,49196,49...\n",
      "320028    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "Name: cipher_list, Length: 5055, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Regex pattern to find the ssl_client_cipher_list array\n",
    "pattern = r'\"ssl_client_cipher_list\":\\[(.*?)\\]'\n",
    "\n",
    "# Function to extract cipher list using regex\n",
    "def extract_cipher_list_regex(json_string):\n",
    "    match = re.search(pattern, json_string)\n",
    "    if match:\n",
    "        return match.group(1)  # Return the capturing group that contains the cipher list numbers\n",
    "    else:\n",
    "        return \"\"  # Return an empty string or some default value if no match is found\n",
    "\n",
    "# Apply the regex extraction method to each row in the @rawstring column\n",
    "df['cipher_list'] = df['@rawstring'].apply(extract_cipher_list_regex)\n",
    "\n",
    "# Display the updated DataFrame\n",
    "non_empty_rows = df[df['cipher_list'].str.len() > 0]\n",
    "print(non_empty_rows['cipher_list'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cipher_list</th>\n",
       "      <th>bytes</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>396.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>845.0</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>51.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>253.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>1242.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td></td>\n",
       "      <td>4675.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td></td>\n",
       "      <td>4786.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td></td>\n",
       "      <td>4820.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td></td>\n",
       "      <td>4823.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>71299.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           cipher_list    bytes  count\n",
       "2                                                         396.0    150\n",
       "201  49196,49195,49200,49199,159,158,49188,49187,49...    845.0    149\n",
       "0                                                          51.0     23\n",
       "1                                                         253.0     23\n",
       "8                                                        1242.0      5\n",
       "..                                                 ...      ...    ...\n",
       "90                                                       4675.0      1\n",
       "91                                                       4786.0      1\n",
       "92                                                       4820.0      1\n",
       "93                                                       4823.0      1\n",
       "205  49196,49195,49200,49199,159,158,49188,49187,49...  71299.0      1\n",
       "\n",
       "[206 rows x 3 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.4') & \\\n",
    "    (df['dest_ip'].str.contains('162.125')), \\\n",
    "        ['src_ip', 'dest_ip', 'dest_port', 'app', \\\n",
    "            'cipher_list', 'bytes']].groupby(['cipher_list',\\\n",
    "                'bytes']).size().reset_index(name='count').\\\n",
    "                    sort_values(by='count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correspond to Listing 7.34 Humio search output – List the values of fields bytes and ssl_client_cipher_list for connections between 10.0.0.18 and IP addresses that contain 162.125 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cipher_list</th>\n",
       "      <th>bytes</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>396.0</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>845.0</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>234.0</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>354.0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>854.0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td></td>\n",
       "      <td>9610.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td></td>\n",
       "      <td>9598.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td></td>\n",
       "      <td>9591.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td></td>\n",
       "      <td>9590.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>49200,49192,49172,49199,49191,49171,159,107,57...</td>\n",
       "      <td>5878.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>447 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           cipher_list   bytes  count\n",
       "9                                                        396.0    236\n",
       "430  49196,49195,49200,49199,159,158,49188,49187,49...   845.0    168\n",
       "2                                                        234.0    114\n",
       "4                                                        354.0     82\n",
       "408  49196,49195,49200,49199,159,158,49188,49187,49...   854.0     65\n",
       "..                                                 ...     ...    ...\n",
       "193                                                     9610.0      1\n",
       "192                                                     9598.0      1\n",
       "191                                                     9591.0      1\n",
       "190                                                     9590.0      1\n",
       "446  49200,49192,49172,49199,49191,49171,159,107,57...  5878.0      1\n",
       "\n",
       "[447 rows x 3 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.18') & \\\n",
    "    (df['dest_ip'].str.contains('162.125')), \\\n",
    "        ['src_ip', 'dest_ip', 'dest_port', 'app', \\\n",
    "            'cipher_list', 'bytes']].groupby(['cipher_list',\\\n",
    "                'bytes']).size().reset_index(name='count').\\\n",
    "                    sort_values(by='count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correspond to Listing 7.37 Humio search code – Find source IP addresses and destination ports for connections to 40.87.160.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>src_ip</th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>dest_port</th>\n",
       "      <th>bytes</th>\n",
       "      <th>data_packets_out</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0.0.12</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0.0.12</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0.0.13</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0.0.15</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.0.0.15</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.0.0.16</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0.0.16</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0.0.18</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0.0.4</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.0.0.9</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.0.0.9</td>\n",
       "      <td>40.87.160.0</td>\n",
       "      <td>23456.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "       src_ip      dest_ip  dest_port  bytes  data_packets_out  count\n",
       "0   10.0.0.12  40.87.160.0    23456.0  114.0               0.0      6\n",
       "1   10.0.0.12  40.87.160.0    23456.0  140.0               0.0    196\n",
       "2   10.0.0.13  40.87.160.0    23456.0  114.0               0.0      2\n",
       "3   10.0.0.13  40.87.160.0    23456.0  140.0               0.0    167\n",
       "4   10.0.0.15  40.87.160.0    23456.0  114.0               0.0      1\n",
       "5   10.0.0.15  40.87.160.0    23456.0  140.0               0.0    194\n",
       "6   10.0.0.16  40.87.160.0    23456.0  114.0               0.0      2\n",
       "7   10.0.0.16  40.87.160.0    23456.0  140.0               0.0    156\n",
       "8   10.0.0.18  40.87.160.0    23456.0  114.0               0.0      7\n",
       "9   10.0.0.18  40.87.160.0    23456.0  140.0               0.0    191\n",
       "10   10.0.0.4  40.87.160.0    23456.0  114.0               0.0      3\n",
       "11   10.0.0.4  40.87.160.0    23456.0  140.0               0.0    145\n",
       "12   10.0.0.8  40.87.160.0    23456.0  114.0               0.0      3\n",
       "13   10.0.0.8  40.87.160.0    23456.0  140.0               0.0    162\n",
       "14   10.0.0.9  40.87.160.0    23456.0  114.0               0.0      2\n",
       "15   10.0.0.9  40.87.160.0    23456.0  140.0               0.0    170"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['dest_ip'].str.contains('40.87.160.0')), \\\n",
    "        ['src_ip', 'dest_ip', 'dest_port', 'bytes', \\\n",
    "            'data_packets_out']].groupby(['src_ip', \\\n",
    "                'dest_ip', 'dest_port', 'bytes', \\\n",
    "            'data_packets_out']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answers to Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correspond to Listing 7.40 Humio search code – Top 10 destination IP addresses starting with 162.125 communicating with 10.0.0.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original = pd.read_json(\"ch7_stream_events.json\")\n",
    "df = df_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>162.125.2.14</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162.125.19.131</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>162.125.4.14</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>162.125.2.13</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>162.125.7.20</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>162.125.19.9</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162.125.19.130</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>162.125.8.20</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>162.125.2.19</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>162.125.4.13</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dest_ip  count\n",
       "7     162.125.2.14    476\n",
       "3   162.125.19.131    174\n",
       "18    162.125.4.14    162\n",
       "6     162.125.2.13    102\n",
       "34    162.125.7.20     90\n",
       "5     162.125.19.9     88\n",
       "2   162.125.19.130     86\n",
       "43    162.125.8.20     86\n",
       "10    162.125.2.19     64\n",
       "17    162.125.4.13     60"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original.loc[(df_original['src_ip'] == '10.0.0.18') & \\\n",
    "    (df_original['dest_ip'].str.contains('162.125')), \\\n",
    "        ['dest_ip']].groupby(['dest_ip']).size().reset_index(name='count').\\\n",
    "                    sort_values(by='count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correspond to Listing 7.41 Humio search output – Top 10 destination IP addresses starting with 162.125 communicating with 10.0.0.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53132     49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "53157     49196,49195,49200,49199,49188,49187,49192,4919...\n",
      "53170     49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "53175     4866,4867,4865,49196,49200,159,52393,52392,523...\n",
      "53218     49196,49195,49200,49199,49188,49187,49192,4919...\n",
      "                                ...                        \n",
      "319760    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "319807    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "319814    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "319870    64250,4865,4866,4866,4867,49195,49199,49196,49...\n",
      "320028    49196,49195,49200,49199,159,158,49188,49187,49...\n",
      "Name: cipher_list, Length: 5055, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Regex pattern to find the ssl_client_cipher_list array\n",
    "pattern = r'\"ssl_client_cipher_list\":\\[(.*?)\\]'\n",
    "\n",
    "# Function to extract cipher list using regex\n",
    "def extract_cipher_list_regex(json_string):\n",
    "    match = re.search(pattern, json_string)\n",
    "    if match:\n",
    "        return match.group(1)  # Return the capturing group that contains the cipher list numbers\n",
    "    else:\n",
    "        return \"\"  # Return an empty string or some default value if no match is found\n",
    "\n",
    "# Apply the regex extraction method to each row in the @rawstring column\n",
    "df['cipher_list'] = df['@rawstring'].apply(extract_cipher_list_regex)\n",
    "\n",
    "# Display the updated DataFrame\n",
    "non_empty_rows = df[df['cipher_list'].str.len() > 0]\n",
    "print(non_empty_rows['cipher_list'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dest_ip</th>\n",
       "      <th>cipher_list</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162.125.2.14</td>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162.125.4.14</td>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162.125.8.14</td>\n",
       "      <td>49196,49195,49200,49199,159,158,49188,49187,49...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dest_ip                                        cipher_list  count\n",
       "0  162.125.2.14  49196,49195,49200,49199,159,158,49188,49187,49...    118\n",
       "1  162.125.4.14  49196,49195,49200,49199,159,158,49188,49187,49...     40\n",
       "2  162.125.8.14  49196,49195,49200,49199,159,158,49188,49187,49...     10"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.18') & \\\n",
    "    (df['dest_ip'].str.contains('162.125')) & \\\n",
    "            (df['bytes']==845), ['src_ip', 'dest_ip', 'dest_port', 'app', \\\n",
    "            'cipher_list', 'bytes']].groupby(['dest_ip',\\\n",
    "                'cipher_list']).size().reset_index(name='count').\\\n",
    "                    sort_values(by='count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.44 Jupyter notebook code — Plot the value of time_diff_sec for connections between 10.0.0.18 and 162.125.4.14 with 845 bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_original = pd.read_json(\"ch7_stream_events.json\")\n",
    "df = df_original\n",
    "\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'], format='mixed')\n",
    "df['endtime'] = pd.to_datetime(df['endtime'], format='mixed')\n",
    "df['epoch_timestamp'] = df['timestamp'].astype('int64') // 10**9\n",
    "df['epoch_endtime'] = df['endtime'].astype('int64') // 10**9\n",
    "\n",
    "df = df.sort_values(by=['epoch_timestamp'], ascending=True)\n",
    "df['epoch_timestamp'] = df['epoch_timestamp'].astype(int)\n",
    "df['epoch_endtime'] = df['epoch_endtime'].astype(int)\n",
    "df['bytes'] = df['bytes'].astype(int)\n",
    "df['bytes_in'] = df['bytes_in'].astype(int)\n",
    "df.dtypes\n",
    "\n",
    "df['time_diff_sec'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['epoch_timestamp'].transform(lambda x: x - x.shift(1))\n",
    "\n",
    "df['lower_quartile'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform(lambda x: x.quantile(q=0.25))\n",
    "df['upper_quartile'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform(lambda x: x.quantile(q=0.75))\n",
    "\n",
    "df['std1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('std')\n",
    "df['var1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('var')\n",
    "df['count1'] = df.groupby(['src_ip', 'dest_ip', 'dest_port'])\\\n",
    "    ['time_diff_sec'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='epoch_timestamp'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.18') &(df['dest_ip'] == '162.125.4.14') & \\\n",
    "       (df['bytes'] == 845)].sort_values(by=['epoch_timestamp'], \\\n",
    "              ascending = True).set_index('epoch_timestamp')\\\n",
    "                     ['time_diff_sec'].plot(figsize=[25,5], \\\n",
    "                            kind = 'line', color = 'orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 7.45 Jupyter notebook code — Plot the value of time_diff_sec for connections between 10.0.0.18 and 162.125.8.14 with 845 bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='epoch_timestamp'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['src_ip'] == '10.0.0.18') &(df['dest_ip'] == '162.125.8.14') & \\\n",
    "       (df['bytes'] == 845)].sort_values(by=['epoch_timestamp'], \\\n",
    "              ascending = True).set_index('epoch_timestamp')\\\n",
    "                     ['time_diff_sec'].plot(figsize=[25,5], \\\n",
    "                            kind = 'line', color = 'orange')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
